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The computer-assisted music composition is an active research area since mid-1900. In this paper, we have applied the VOGUE model for designing musical sequence of bandish notations of raga Bhairav, a classical Indian music. Variable Order and Gapped hidden Markov model for unstructured elements can capture variable length dependencies with variable gaps in sequential data. In most of raga pattern, a particular pattern repeats itself which may be separated by variable length gaps. VOGUE mines the frequent patterns in raga having different length gaps. These mined patterns are used to model VOGUE for Indian music ragas. Furthermore, we analyzed the benefits of VOGUE model over the standard HMM. To the best of author's knowledge, this is the very first attempt to model Indian classical music with variable order gapped HMM. [ABSTRACT FROM AUTHOR]

Abstract 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.

As a promising field of research, event sequence analysis seems to assist in facilitating clear reasoning behind human decisions by mining reality behind the sequential actions. Mining frequent patterns from event sequences has proved to be promising in extracting actionable insights, which plays an important role in many application domains. Much of the related work challenges the problem solely from the temporal perspective omitting the information that could be gained from the spatial part. This could be in part due to the fact that analysis of event sequences with references to both time and space is attributed as a challenging task due to the additional variance in the data introduced by the spatial aspect. We propose a visual analytics approach that incorporates spatio-temporal pattern extraction leveraging an extended sequential pattern mining algorithm and a pattern discovery guidance mechanism operating on geographic query and selection capabilities. As an implementation of our approach, we introduce a visual analytics tool, namely ST Sequence Miner, enabling event pattern exploration in time-location space. We evaluate our approach over a credit card transaction dataset by adopting case study methodology. Our study unveils that patterns mined from event sequences can better explain possible relationships with proper visualization of time-location data. [ABSTRACT FROM AUTHOR]

Recently, a lot of research work has been proposed in different domains to detect outliers and analyze the outlierness of outliers for relational data. However, while sequence data is ubiquitous in real life, analyzing the outlierness for sequence data has not received enough attention. In this article, we study the problem of mining outlying sequence patterns in sequence data addressing the question: given a query sequence s in a sequence dataset D, the objective is to discover sequence patterns that will indicate the most unusualness (i.e., outlierness) of s compared against other sequences. Technically, we use the rank defined by the average probabilistic strength (aps) of a sequence pattern in a sequence to measure the outlierness of the sequence. Then a minimal sequence pattern where the query sequence is ranked the highest is defined as an outlying sequence pattern. To address the above problem, we present OSPMiner, a heuristic method that computes aps by incorporating several pruning techniques. Our empirical study using both real and synthetic data demonstrates that OSPMiner is effective and efficient. [ABSTRACT FROM AUTHOR]

We propose a hybrid application of Population Based Ant Colony Optimization that uses a data mining procedure to wisely initialize the pheromone entries. Hybridization of metaheuristics with data mining techniques has been studied by several researchers in recent years. In this line of research, frequent patterns in a number of initial high-quality solutions are extracted to guide the subsequent iterations of an algorithm, which results in an improvement in solution quality and computational time. Our proposal possesses certain differences from and contributions to existing literature. Instead of one single run that incorporates both the main metaheuristic and the data mining module inside, we propose to carry out independent runs and collect elite sets over these trials. Another contribution is the way we use the knowledge gained from the application of the data mining module. The extracted knowledge is used to initialize the memory model in the algorithm rather than to construct new initial solutions. One additional contribution is the use of a path mining algorithm (a specific sequence mining algorithm) rather than Apriori-like association mining algorithms. Computational experiments, conducted both on symmetric Travelling Salesman Problem and symmetric/asymmetric Quadratic Assignment Problem instances, showed that our proposal produces significantly better results, and is more robust than pure applications of population-based ant colony optimization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hypleurochilus delimited into two clades and one lineage. • The phylogenetic relationships within Hypleurochilus reflect biogeographic breaks. • Single-locus delimitation fails to resolve recently diverged species. • First report of sister relationship between H. caudovittatus and H. multifilis. • Mined sequence data supports the hypothesis of a range expansion of H. aequipinnis. As some of the smallest vertebrates, yet largest producers of consumed reef biomass, cryptobenthic reef fishes serve a disproportionate role in reef ecosystems and are one of the most poorly understood groups of fish. The blenny genera Hypleurochilus and Parablennius are currently considered paraphyletic and the interrelationships of Parablennius have been the focus of recent phylogenetic studies. However, the interrelationships of Hypleurochilus remain understudied. This genus is transatlantically distributed and comprises 11 species with a convoluted taxonomic history. In this study, relationships for ten Hypleurochilus species are resolved using multi-locus nuclear and mtDNA sequence data, morphological data, and mined COI barcode data. Mitochondrial and nuclear sequence data from 61 individuals collected from the western Atlantic and northern Gulf of Mexico (N. GoM) delimit seven species into a temperate clade, a tropical clade, and a third distinct lineage. This lineage, herein referred to as H. cf. aequipinnis, may represent a species of Hypleurochilus whose range has expanded into the N. GoM. Inclusion of publicly available COI sequence for an additional three species provides further phylogenetic resolution. H. bananensis forms a new eastern Atlantic clade with H. cf. aequipinnis , providing further evidence for a western Atlantic range expansion. Single marker COI delimitation was unable to elucidate the relationships between H. springeri/H. pseudoaequipinnis and between H. multifilis/H. caudovittatus due to incomplete lineage sorting. Mitochondrial data are also unable to accurately resolve the placement of H. bermudensis. However, a comprehensive approach using multi-locus phylogenetic and species delimitation methods was able to resolve these relationships. While mining publicly available sequence data allowed for the inclusion of an increased number of species in the analysis and a more comprehensive phylogeny, it was not without drawbacks, as a handful of sequences are potentially mis-identified. Overall, we find that the recent divergence of some species within this genus and potential introgression events confound the results of single locus delimitation methods, yet a combination of single and multi-locus analyses has allowed for insights into the biogeography of this genus and uncovered a potential transatlantic range expansion. [ABSTRACT FROM AUTHOR]

Abstract Background A common challenge in bioinformatics is to identify short sub-sequences that are unique in a set of genomes or reference sequences, which can efficiently be achieved by k-mer (k consecutive nucleotides) counting. However, there are several areas that would benefit from a more stringent definition of “unique”, requiring that these sub-sequences of length W differ by more than k mismatches (i.e. a Hamming distance greater than k) from any other sub-sequence, which we term the k-disjoint problem. Examples include finding sequences unique to a pathogen for probe-based infection diagnostics; reducing off-target hits for re-sequencing or genome editing; detecting sequence (e.g. phage or viral) insertions; and multiple substitution mutations. Since both sensitivity and specificity are critical, an exhaustive, yet efficient solution is desirable. Results We present microTaboo, a method that allows for efficient and extensive sequence mining of unique (k-disjoint) sequences of up to 100 nucleotides in length. On a number of simulated and real data sets ranging from microbe- to mammalian-size genomes, we show that microTaboo is able to efficiently find all sub-sequences of a specified length W that do not occur within a threshold of k mismatches in any other sub-sequence. We exemplify that microTaboo has many practical applications, including point substitution detection, sequence insertion detection, padlock probe target search, and candidate CRISPR target mining. Conclusions microTaboo implements a solution to the k-disjoint problem in an alignment- and assembly free manner. microTaboo is available for Windows, Mac OS X, and Linux, running Java 7 and higher, under the GNU GPLv3 license, at: https://MohammedAlJaff.github.io/microTaboo

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.

Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea behind predictive pattern mining is to build a prediction model by considering unified inconsistent notation sub-structures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the safe pattern pruning method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

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.

Detecting small sets of relevant patterns from a given data set 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 focus either on scalability or on generality. In this paper, we make steps toward combining local (frequency, size, and cost) and global (various condensed representations like maximal, closed, and 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 data sets 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. [ABSTRACT FROM AUTHOR]

Abstract The accumulation of serial remote sensing images provides plentiful data for discovering sequential spatial patterns in various fields such as agricultural monitoring, urban development, and vegetation cover. Otherwise, traditional sequential pattern-mining algorithms cannot be directly or efficiently applied to remote sensing images. In this study, we propose a pixel clustering-based method to improve the efficiency of mining spatial sequential patterns from raster serial remote sensing images (SRSI). Firstly, the images are compressed by using the Run-Length coding schema. Then, pixels with identical sequences are clustered by means of the Run-length code-based spatial overlay operation. Finally, a pruning strategy is proposed, to extend the prefixSpan algorithm to skip unnecessary database scanning when mining from pixel groups. The experimental results indicate that the method presented in this paper could extract spatial sequential patterns from SRSI efficiently. Although accurate support rates for the patterns may not be obtained, our method could ensure that all patterns are extracted with a lower time cost. Highlights •A Run-Length Coding based pixels clustering method is proposed. •A pruning strategy to extend prefixSpan algorithm is proposed. •The proposed method is efficient for mining spatial sequential patterns. [ABSTRACT FROM AUTHOR]

Accurate cost and time estimation of a query is one of the major success indicators for database management systems. SQL allows the expression of flexible queries on text-formatted data. The LIKE operator is used to search for a specified pattern (e.g., LIKE "luck%") in a string database. It is vital to estimate the selectivity of such flexible predicates for the query optimizer to choose an efficient execution plan. In this paper, we study the problem of estimating the selectivity of a LIKE query predicate over a bag of strings. We propose a new type of pattern-based histogram structure to summarize the data distribution in a particular column. More specifically, we first mine sequential patterns over a given string database and then construct a special histogram out of the mined patterns. During query optimization time, pattern-based histograms are exploited to estimate the selectivity of a LIKE predicate. The experimental results on a real dataset from DBLP show that the proposed technique outperforms the state of the art for generic LIKE queries like %s1%s2%...%sn% where si represents one or more characters. What is more, the proposed histogram structure requires more than two orders of magnitude smaller memory space, and the estimation time is almost an order of magnitude less in comparison to the state of the art. [ABSTRACT FROM AUTHOR]

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.

Nowadays data mining is the way of extracting hidden knowledge from raw data whereas sequence mining aims to find sequential patterns that are frequent in the database, so publishing these data may lead to the disclosure of private information about organizations or individuals. Knowledge hiding is the process of hiding sensitive knowledge extracted previously from the database, to ensure that no abuse will be caused. This paper addresses the problem of sequential pattern hiding and proposes an efficient algorithm which uses a multi-objective approach to overcome the problem of sequence hiding as well as maintaining database fidelity as much as possible. It also shows that the proposed algorithm outperforms existing methods in terms of both speed and memory usage.

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’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’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.