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This article presents a novel methodology to detect insurance claim related frauds in the healthcare system using concepts of sequence mining and sequence prediction. Fraud detection in healthcare is a non-trivial task due to the heterogeneous nature of healthcare records. Fraudsters behave as normal patients and with the passage of time keep on changing their way of planting frauds; hence, there is a need to develop fraud detection models. The sequence generation is not the part of previous researches which mostly focus on amount based analysis or medication versus diseases sequential analysis. The proposed methodology is able to generate sequences of services availed or prescribed by each specialty and analyse via two cascaded checks for the detection of insurance claim related frauds. The methodology addresses these challenges and self learns from historical medical records. It is based on two modules namely “Sequence rule engine and Prediction based engine”. The sequence rule engine generates frequent sequences and probabilities of rare sequences for each specialty of the hospital. The comparison of such sequences with the actual patient sequences leads to the identification of anomalies as both sequences are not compliant to the sequences of the rule engine. The system performs further in detail analysis on all non-compliant sequences in the prediction based engine. The proposed methodology is validated by generating patient sequences from last five years transactional data of a local hospital and identifies patterns of service procedures administered to patients using Prefixspan algorithm and Compact prediction tree. Various experiments have been performed to validate the applicability of the developed methodology and the results demonstrate that the methodology is pertinent to detect healthcare frauds and provides on average 85% of accuracy. Thus can help in preventing fraudulent claims and provides better insight into how to improve patient management and treatment procedures.

Representations of biological sequences facilitating sequence comparison are crucial in several bioinformatics tasks. Recently, the Lyndon factorization has been proved to preserve common factors in overlapping reads, thus leading to the idea of using factorizations of sequences to define measures of similarity between reads. In this paper we propose as a signature of sequencing reads the notion of fingerprint, i.e., the sequence of lengths of consecutive factors in Lyndon-based factorizations of the reads. Surprisingly, fingerprints of reads are effective in preserving sequence similarities while providing a compact representation of the read, and so, k-mers extracted from a fingerprint, called k-fingers, can be used to capture sequence similarity between reads. We first provide a probabilistic framework to estimate the behaviour of fingerprints. Then we experimentally evaluate the effectiveness of this representation for machine learning algorithms for classifying biological sequences. In particular, we considered the problem of assigning RNA-Seq reads to the most likely gene from which they were generated. Our results show that fingerprints can provide an effective machine learning interpretable representation, successfully preserving sequence similarity.

The nature of people's web navigation has significantly changed in recent years. The advent of smartphones and other handheld devices has given rise to web users consulting websites with more than one device, or using a shared device. As a result, large volumes of seemingly disjoint data are available, which when analysed together can support decision-making. The task of identifying web sessions by linking such data back to a specific person, however, is hard. The idea of session stitching aims to overcome this by using machine learning inference to identify similar or identical users. Many such efforts use various demographic data or device-based features to train matching algorithms. However, often these variables are not available for every dataset or are recorded differently, making a streamlined setup difficult. Besides, they often result in vast feature spaces which are hard to use for actionable interpretation. In this paper, we present an alternative approach based on the fingerprinting of web pages visited by users in a single session. By learning behavioural patterns from these sequences of page visits, we obtain features that can be used for matching without requiring sensitive user-agent data such as IP, geo location, or device details as is common with other approaches. Using these sequential fingerprints does not rely on pre-defined features, but only requires the recording of web page visits, making our approach actionable. The approach is empirically tested on real-life web logs and compared with matching using regular user-agent features and state-of-the-art embedding techniques. Results in an ecommerce context show sequential features can still obtain strong performance with fewer features, facilitating decision-making on session stitching and inform subsequent related activities such as marketing or customer analysis.

Learning programming is a complex and challenging task for many students. It involves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from online learning environments alone fails to capture the full breadth of students’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achievement. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an automated assessment tool used to support students while solving the programming assignments. The study included the data of 292 engineering students (228 men and 64 women, aged 20–26) from the two aforementioned sources. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely, slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual&rsquo, s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual&rsquo, s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods.

Continuous training is crucial for creating and maintaining the right skill-profile for the industrial organization’s workforce. There is a tremendous variety in the available trainings within an organization: technical, project management, quality, leadership, domain-specific, soft-skills, etc. Hence it is important to assist the employee in choosing the best trainings, which perfectly suits her background, project needs and career goals. In this paper, we focus on algorithms for training recommendation in an industrial setting. We formalize the problem of next training recommendation, taking into account the employee’s training and work history. We present several new unsupervised sequence mining algorithms to mine the past trainings data from the organization for arriving at personalized next training recommendation. Using the real-life data about trainings of 118,587 employees over 5019 distinct trainings from a large multi-national IT organization, we show that these algorithms outperform several standard recommendation engine algorithms as well as those based on standard sequence mining algorithms.

In elite sports, training schedules are becoming increasingly complex, and a large number of parameters of such schedules need to be tuned to the specific physique of a given athlete. In this paper, we describe how extensive analysis of historical data can help optimise these parameters, and how possible pitfalls of under- and overtraining in the past can be avoided in future schedules. We treat the series of exercises an athlete undergoes as a discrete sequence of attributed events, that can be aggregated in various ways, to capture the many ways in which an athlete can prepare for an important test event. We report on a cooperation with the elite speed skating team LottoNL-Jumbo, who have recorded detailed training data over the last 15 years. The aim of the project was to analyse this potential source of knowledge, and extract actionable and interpretable patterns that can provide input to future improvements in training. We present two alternative techniques to aggregate sequences of exercises into a combined, long-term training effect, one of which based on a sliding window, and one based on a physiological model of how the body responds to exercise. Next, we use both linear modelling and Subgroup Discovery to extract meaningful models of the data.

Prior education studies have consistently emphasized the importance of sustained and active student engagement to aid academic performance and achievement of learning outcomes (e.g., Hockings, Cooke,Yamashita, McGinty, & Bowl, 2008; Michael, 2006). The positive impact of such active learning models on academic outcomes has been well established, particularly, in the STEM (Science, Technology, Engineering and Mathematics) disciplines. Refereed/Peer-reviewed

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals’ learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two different methods of extracting study patterns from activity sequences. Unlike most of the previous works, with post hoc analysis of activity patterns, our proposed methods could be deployed during the course and enable the learners to receive real-time support and feedback. In the first method, following a hypothesis-driven approach, we extract predefined patterns from learners’ interactions with the course materials. We then identify and analyze different longitudinal profiles among learners by clustering their study pattern sequences during the course. Our second method is a data-driven approach to discover latent study patterns and track them over time in a completely unsupervised manner. We propose a clustering pipeline to model and cluster activity sequences at each time step and then search for matching clusters in previous steps to enable tracking over time. The proposed pipeline is general and allows for analysis at different levels of action granularity and time resolution in various online learning environments. Experiments with synthetic data show that our proposed method can accurately detect latent study patterns and track changes in learning behaviours. We demonstrate the application of both methods on a MOOC dataset and study the temporal dynamics of learners’ behaviour in this context.

Chatbots have been used in business contexts as a new way of communicating with customers. They use natural language to interact with the customers, whether while offering products and services, or in the support of a specific task. In this context, an important and challenging task is to assess the effectiveness of the machine-to-human interaction, according to business’ goals. Although several analytic tools have been proposed to analyze the user interactions with chatbot systems, to the best of our knowledge they do not consider user-defined criteria, focusing on metrics of engagement and retention of the system as a whole. For this reason, we propose the KnowBots tool, which can be used to discover relevant patterns in the dialogues of chatbots, by considering specific business goals. Given the non-trivial structure of dialogues and the possibly large number of conversational records, we combined sequential pattern mining and subgroup discovery techniques to identify patterns of usage. Moreover, a friendly user-interface was developed to present the results and to allow their detailed analysis. Thus, it may serve as an alternative decision support tool for business or any entity that makes use of this type of interactions with their clients.

Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the Item2Item model to improve the prediction accuracy. As a case study, this paper focuses on user behaviour prediction in restaurant recommender systems and uses a public dataset including restaurant information and user sessions. Within this dataset, user behaviour can be discovered for the collaborative filtering, and restaurant information is extracted for the content-based filtering. The idea of the pre-trained word embedding in Natural Language Processing is utilized in the item-based collaborative filtering to find the similarity between restaurants based on user sessions. Experimental results have shown that the combination of these techniques makes valuable recommendations.

Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.

This paper examines the use of “pacing plots” to represent variations in student learning sequences within a digital curriculum. Pacing plots are an intuitive and flexible data visualizations that have a potential for revealing the diversity of blended classroom instructional models. By using curriculum pacing plots, we identified several common implementation patterns in real-world classrooms. After analyzing two years’ worth of data from over 150,000 students in a digital math curriculum, we found that a PCA and K-Means clustering approach was able to discover pedagogically relevant instructional practices.

Finding frequent sequential patterns has been defined as finding ordered list of items that occur more times in a database than a user defined threshold. For big and dense databases that contain really long sequences and large itemset such as medical case histories, algorithm proposed on this idea of counting the occurrences output enourmous number of highly redundant frequent sequences, and are therefore simply impractical. Therefore, there is a need for algorithm that perform frequent pattern search and prefiltering simultaneously. In this paper, we propose an algorithm that reinterprets the term support on text mining basis. Experiments show that our method not only eliminates redundancy among the output sequences, but it scales much better with huge input data sizes. We apply our algorithm for mining medical databases: what diagnoses are likely to lead to a certain future health condition.

Stream processing has become extremely popular for analyzing huge volumes of data for a variety of applications, including IoT, social networks, retail, and software logs analysis. Streams of data are produced continuously and are mined to extract patterns characterizing the data. A class of data mining algorithm, called generate-and-test , produces a set of candidate patterns that are then evaluated over data. The main challenges of these algorithms are to achieve high throughput, low latency, and reduced power consumption. In this article, we present a novel power-efficient, fast, and versatile hardware architecture whose objective is to monitor a set of target patterns to maintain their frequency over a stream of data. This accelerator can be used to accelerate data-mining algorithms, including itemsets and sequences mining. The massive fine-grain reconfiguration capability of field-programmable gate array (FPGA) technologies is ideal to implement the high number of pattern-detection units needed for these intensive data-mining applications. We have thus designed and implemented an IP that features high-density FPGA occupation and high working frequency. We provide detailed description of the IP internal micro-architecture and its actual implementation and optimization for the targeted FPGA resources. We validate our architecture by developing a co-designed implementation of the Apriori Frequent Itemset Mining (FIM) algorithm, and perform numerous experiments against existing hardware and software solutions. We demonstrate that FIM hardware acceleration is particularly efficient for large and low-density datasets (i.e., long-tailed datasets). Our IP reaches a data throughput of 250 million items/s and monitors up to 11.6k patterns simultaneously, on a prototyping board that overall consumes 24W in the worst case. Furthermore, our hardware accelerator remains generic and can be integrated to other generate and test algorithms.

The pervasiveness of location based services such the GPS on mobile devices enabled the gathering of massive spatial-temporal data. These databases enabled the mining of new data in order to calculate frequent patterns and predict the movement of the objects. In the development of our system for guiding the user with cognitive disabilities (CogHelper) we are applying a trajectory data mining to adapt and adjust the path to user preferences. Indeed, the guiding process may be more useful and it increases the quality of life of the user through this new functionality (conciliated with the speculative computation module). Thus, instead of the user having to adapt to the application we are developing a system that adapts the path to the user. Rather than being guided through the shortest path he may be oriented by a longer but preferred path. The main contribution of this paper is the specification of the trajectory data mining which is incorporated in CogHelper system., This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e a Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of João Ramos is supported by a FCT doctoral grant with the reference SFRH/BD/89530/2012., info:eu-repo/semantics/publishedVersion

In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user’s location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.

The efficiency of Public Transportation (PT) Networks is a major goal of any urban area authority. Advances on both location and communication devices drastically increased the availability of the data generated by their operations. Adequate Machine Learning methods can thus be applied to identify patterns useful to improve the Schedule Plan. In this paper, the authors propose a fully automated learning framework to determine the best Schedule Coverage to be assigned to a given PT network based on Automatic Vehicle location (AVL) and Automatic Passenger Counting (APC) data. We formulate this problem as a clustering one, where the best number of clusters is selected through an ad-hoc metric. This metric takes into account multiple domain constraints, computed using Sequence Mining and Probabilistic Reasoning. A case study from a large operator in Sweden was selected to validate our methodology. Experimental results suggest necessary changes on the Schedule coverage. Moreover, an impact study was conducted through a large-scale simulation over the affected time period. Its results uncovered potential improvements of the schedule reliability on a large scale.

HighlightsPrescribing cascade can be associated with adverse effects.Timely detection of detrimental prescribing cascades is essential.Social media is a promising source to detect detrimental prescribing cascades.Sequence mining is used to signal detrimental prescribing cascades from social media. MotivationPrescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths. Identifying detrimental PCs would enable warnings and contraindications to be disseminated and assist the detection of unknown drug AEs. Nonetheless, the detection is difficult and has been limited to case reports or case assessment using administrative health claims data. Social media is a promising source for detecting signals of detrimental PCs due to the public availability of many discussions regarding treatments and drug AEs. ObjectiveIn this paper, we investigate the feasibility of detecting detrimental PCs from social media. MethodsThe detection, however, is challenging due to the data uncertainty and data rarity in social media. We propose a framework to mine sequences of drugs and AEs that signal detrimental PCs, taking into account the data uncertainty and data rarity. ResultsWe conduct experiments on two real-world datasets collected from Twitter and Patient health forum. Our framework achieves encouraging results in the validation against known detrimental PCs (F1=78% for Twitter and 68% for Patient) and the detection of unknown potential detrimental PCs ([email protected]=72% and [email protected]=95% for Twitter, [email protected]=86% and [email protected]=98% for Patient). In addition, the framework is efficient and scalable to large datasets. ConclusionOur study demonstrates the feasibility of generating hypotheses of detrimental PCs from social media to reduce pharmacists' guesswork.

The goal of constraint-based sequence mining is to find sequences of symbols that are included in a large number of input sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four categories of constraints that are applicable to sequence mining. We then propose two constraint programming formulations. The first formulation introduces a new global constraint called exists-embedding. This formulation is the most efficient but does not support one type of constraint. To support such constraints, we develop a second formulation that is more general but incurs more overhead. Both formulations can use the projected database technique used in specialised algorithms. Experiments demonstrate the flexibility towards constraint-based settings and compare the approach to existing methods., In Integration of AI and OR Techniques in Constraint Programming (CPAIOR), 2015

Sequence mining combines the discovery of frequent itemsets and the order they appear in. Most of the sequence pattern discovery techniques present some handicaps like the generation of a huge number of rules and the lack of scalability. In this work the proposed algorithm concerns the analysis of the whole rather than the parts, thus providing a holistic view of the sequences. The algorithm analyzes event logs and allows a non-expert user to understand the sequences using a poly-tree visualization. The scalability associated with condensed data structures, which shrink the data without losing information, allows dealing with the Big Data challenge. Ramex was implemented in different scenarios.

Sequential pattern mining is an important data mining problem widely addressed by the data mining community, with a very large field of applications. The sequence pattern mining aims at extracting a set of attributes, shared across time among a large number of objects in a given database. The work presented in this paper is directed towards the general theoretical foundations of the pattern-growth approach. It helps indepth understanding of the pattern-growth approach, current status of provided solutions, and direction of research in this area. In this paper, this study is carried out on a particular class of pattern-growth algorithms for which patterns are grown by making grow either the current pattern prefix or the current pattern suffix from the same position at each growth-step. This study leads to a new algorithm called prefixSuffixSpan. Its correctness is proven and experimentations are performed.

International audience; Learning Analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a speci c data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students' enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process.

In this paper we describe two usability studies in which the interaction of users with an online application was remotely and automatically captured and analyzed. The usability studies were performed using the WebHint method for usability analysis of web applications. We evaluate two different versions of the application in order to observe the applicability of the method for successive usability analysis. The results show how the WebHint method can be used as an alternative approach to carry out successive evaluations of the usability of an application in order to analyze the evolution of different versions of its interface.

In this paper we present a novel approach for analyzing the trajectories of moving objects and of people in particular. The minded data from these sequences can provide valuable information for understanding the surrounding locations, discovering attractive place or mining frequent sequences of visited places. Based on geotagged photos, our frameworkmines semantically annotated sequences. Our framework is capable of mining semantically annotated sequences of any length to discover patterns that are not necessarily immediate antecedents. The approach consists of four main steps. In the first step, every photo location is semantically annotated by assigning it to a known nearby point of interest. In thesecond step, a density-based clustering algorithm is applied to all unassigned photos, creating regions of unknown points of interest. In the third step, a travel sequence of every individual is built. In the final step, travel sequence patterns are mined using the semantics that were obtained from the first two steps. Case studies of Guimarães, Portugal (where the conference takes place) and Berlin, Germany demonstrate the capabilities of the proposed framework.

International audience; Many applications require techniques for temporal knowledge discovery. Some of those approaches can handle time constraints between events. In particular some work has been done to mine generalized sequential patterns. However, such constraints are often too crisp or need a very precise assessment to avoid erroneous information. Therefore, in this paper we propose to soften temporal constraints used for generalized sequential pattern mining. To handle these constraints while data mining, we design an algorithm based on sequence graphs. Moreover, as these relaxed constraints may extract more generalized patterns, we propose temporal accuracy measure for helping the analysis of the numerous discovered patterns.

Sequential pattern mining allows to discover temporal relationship between items within a database. The patterns can then be used to generate association rules. When the databases are very large, the execution speed and the memory usage of the mining algorithm become critical parameters. Previous research has focused on either one of the two parameters. In this paper, we present bitSPADE, a novel algorithm that combines the best features of SPAM, one of the fastest algorithm, and SPADE, one of the most memory efficient algorithm. Moreover, we introduce a new pruning strategy that enables bitSPADE to reach high performances. Experimental evaluations showed that bitSPADE ensures an efficient tradeoff between speed and memory usage by outperforming SPADE by both speed and memory usage factors more than 3.4 and SPAM by a memory consumption factor up to more than an order of magnitude.

Open and distance learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organization theory. It describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach.

In this paper, we discuss an approach for discovering temporal changes in event sequences, and present first results from a study on demographic data. The data encode characteristic events in a person's life course, such as their birth date, the begin and end dates of their partnerships and marriages, and the birth dates of their children. The goal is to detect significant changes in the chronology of these events over people from different birth cohorts. To solve this problem, we encoded the temporal information in a first-order logic representation, and employed Warmr, an ILP system that discovers association rules in a multi-relational data set, to detect frequent patterns that show significant variance over different birth cohorts. As a case study in multi-relational association rule mining, this work illustrates the flexibility resulting from the use of first-order background knowledge, but also uncovers a number of important issues that hitherto received little attention. ispartof: pages:29-41 ispartof: Principles of Data Mining and Knowledge Discovery, PKDD 2001 vol:2168 pages:29-41 ispartof: European Conference on Principles and Practice of Knowledge Discovery in Databases location:Freiburg, Germany date:3 Sep - 7 Sep 2001 status: published

Huge amount of information is available in un-structured (text) documents. Knowledge discovery in un-structured document has been recognized as promising task in the recent years. Since un-structured document is typically formatted for human viewing, it varies widely from document to document. Frequent changes made to their formatting further causes difficulty in construction of a global schema. So, Discovery of interesting rules form it is complex and tedious process. Most of the existing system uses hand-coded wrappers to extract information, which is monotonous and time consuming. In this paper we propose a novel and hybrid approach of learning (context-free) grammar rules that are based on alignment between texts. Also it automatically discovers the grammar rules using grammatical inference of repeated pattern present in un-structured (text) document. The generated rules can be used to infer the attribute value pairs from the unstructured text document.

Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP)., 29 pages, 7 figures, 5 tables