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Robust frequent itemset mining has attracted much attention due to the necessity to find frequent patterns from noisy data in many applications. In this paper, we focus on a variant of robust frequent itemsets in which a small amount of “faults” is allowed in each item and each supporting transaction. This problem is challenging since computing fault-tolerant support count is NP-hard and the anti-monotone property does not hold when the amount of allowable faults is proportional to the size of the itemset. We develop heuristic methods to solve an approximation version of the problem and propose speedup techniques for the exact problem. Experimental results show that our heuristic algorithms are substantially faster than the state-of-the-art exact algorithms while the error is acceptable. In addition, the proposed speedup techniques substantially improve the efficiency of the exact algorithms. [ABSTRACT FROM AUTHOR]

High-utility itemset mining aims to find the set of items with utility no less than a user-defined threshold in a transaction database. High-utility itemset mining is an emerging research area in the field of data mining and has important applications in inventory management, query recommendation, systems operation research, bio-medical analysis, etc. Currently, known algorithms for this problem can be classified as either 1-phase or 2-phase algorithms. The 2-phase algorithms typically consist of tree-based algorithms which generate candidate high-utility itemsets and verify them later. A tree data structure generates candidate high-utility itemsets quickly by storing some upper bound utility estimate at each node. The 1-phase algorithms typically consist of inverted-list based and transaction projection based algorithms which avoid the generation of candidate high-utility itemsets. The inverted list and transaction projection allows computation of exact utility estimates. We propose a novel hybrid framework that combines a tree-based and an inverted-list based algorithm to efficiently mine high-utility itemsets. Algorithms based on the framework can harness benefits of both types of algorithms. We report experiment results on real and synthetic datasets to demonstrate the effectiveness of our framework. [ABSTRACT FROM AUTHOR]

Anonymization of graph-based data is a problem, which has been widely studied last years, and several anonymization methods have been developed. Information loss measures have been carried out to evaluate the noise introduced in the anonymized data. Generic information loss measures ignore the intended anonymized data use. When data has to be released to third-parties, and there is no control on what kind of analyses users could do, these measures are the standard ones. In this paper we study different generic information loss measures for graphs comparing such measures to the cluster-specific ones. We want to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes. [ABSTRACT FROM AUTHOR]

Classification algorithms are the most commonly used data mining models that are widely used to extract valuable knowledge from huge amounts of data. The criteria used to evaluate the classifiers are mostly accuracy, computational complexity, robustness, scalability, integration, comprehensibility, stability, and interestingness. This study compares the classification of algorithm accuracies, speed (CPU time consumed) and robustness for various datasets and their implementation techniques. The data miner selects the model mainly with respect to classification accuracy; therefore, the performance of each classifier plays a crucial role for selection. Complexity is mostly dominated by the time required for classification. In terms of complexity, the CPU time consumed by each classifier is implied here. The study first discusses the application of certain classification models on multiple datasets in three stages: first, implementing the algorithms on original datasets; second, implementing the algorithms on the same datasets where continuous variables are discretised; and third, implementing the algorithms on the same datasets where principal component analysis is applied. The accuracies and the speed of the results are then compared. The relationship of dataset characteristics and implementation attributes between accuracy and CPU time is also examined and debated. Moreover, a regression model is introduced to show the correlating effect of dataset and implementation conditions on the classifier accuracy and CPU time. Finally, the study addresses the robustness of the classifiers, measured by repetitive experiments on both noisy and cleaned datasets. [ABSTRACT FROM AUTHOR]

In many real-world applications, information such as web click data, stock ticker data, sensor network data, phone call records, and traffic monitoring data appear in the form of data streams. Online monitoring of data streams has emerged as an important research undertaking. Estimating the frequency of the items on these streams is an important aggregation and summary technique for both stream mining and data management systems with a broad range of applications. This paper reviews the state-of-the-art progress on methods of identifying frequent items from data streams. It describes different kinds of models for frequent items mining task. For general models such as cash register and Turnstile, we classify existing algorithms into sampling-based, counting-based, and hashing-based categories. The processing techniques and data synopsis structure of each algorithm are described and compared by evaluation measures. Accordingly, as an extension of the general data stream model, four more specific models including time-sensitive model, distributed model, hierarchical and multi-dimensional model, and skewed data model are introduced. The characteristics and limitations of the algorithms of each model are presented, and open issues waiting for study and improvement are discussed. [ABSTRACT FROM AUTHOR]