Conceptual Coverage Driven by Essential Concepts: A Formal Concept Analysis Approach

Formal concept analysis (FCA) is a mathematical theory that is typically used as a knowledge representation method. The approach starts with an input binary relation specifying a set of objects and attributes, finds the natural groupings (formal concepts) described in the data, and then organizes the concepts in a partial order structure or concept (Galois) lattice. Unfortunately, the total number of concepts in this structure tends to grow exponentially as the size of the data increases. Therefore, there are numerous approaches for selecting a subset of concepts to provide full or partial coverage. In this paper, we rely on the battery of mathematical models offered by FCA to introduce a new greedy algorithm, called Concise, to compute minimal and meaningful subsets of concepts. Thanks to its theoretical properties, the Concise algorithm is shown to avoid the sluggishness of its competitors while offering the ability to mine both partial and full conceptual coverage of formal contexts. Furthermore, experiments on massive datasets also underscore the preservation of the quality of the mined formal concepts through interestingness measures agreed upon by the community.

Subgroup Discovery in Structural Equation Model

Structural equation modeling (SEM) is one of the most popular statistical frameworks in the social and behavioural sciences. Often, detection of groups with distinct sets ofparameters in structural equation models (SEM) are of key importance for appliedresearchers, for example, when investigating differential item functioning for a mentalability test or examining children with exceptional educational trajectories. In this paper, we present a new approach combining subgroup discovery – a well-established toolkit of supervised learning algorithms and techniques from the field of computer science – with structural equation models. We provide an introduction how subgroup discovery can be applied to detect subgroups with exceptional parameter constellations in structural equation models based on user-defined interestingness measures. Furthermore, technical details on the algorithmic components, efficiency, and further computational aspects are presented. Then, our approach is illustrated with two real-world data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied bya short introduction in the R package subgroupsem, which is a viable implementation of our approach for applied researchers.

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

Parallel Approaches of Utility Mining for Big Data

Utility Itemset Mining (UIM) is a fundamental technique to find out various itemsets with interestingness measures in addition to their quantity. It helps in finding valuable items that cannot be tracked with frequent itemset mining. There are many techniques to mine the itemsets based on their utilities, but the need of the hour is to mine them from larger datasets. This paper presents a brief overview of various approaches for utility mining, which mine using the parallel framework to enhance the pace of computation. The paper is concluded with a discussion on various challenges and openings in the field of parallel mining and provides away for further development of the prevailing methodologies of big data.

The selection of the relevant association rules using the ELECTRE method with multiple criteria

The extraction of association rules is a very attractive data mining task and the most widespread in the business world and in modern society, trying to obtain the interesting relationship and connection between collections of articles, products or items in high transactional databases. The immense quantity of association rules obtained expresses the main obstacle that a decision maker can handle. Consequently, in order to establish the most interesting association rules, several interestingness measures have been introduced. Currently, there is no optimal measure that can be chosen to judge the selected association rules. To avoid this problem we suggest to apply ELECTRE method one of the multi-criteria decision making, taking into consideration a formal study of measures of interest according to structural properties, and intending to find a good compromise and select the most interesting association rules without eliminating any measures. Experiments conducted on reference data sets show a significant improvement in the performance of the proposed strategy.

A FRAMEWORK FOR INCREMENTAL PARALLEL MINING OF INTERESTING ASSOCIATION PATTERNS FOR BIG DATA

Association rule mining plays a very important role in the distributed environment for Big Data analysis. The massive volume of data creates imminent needs to design novel, parallel and incremental algorithms for the association rule mining in order to handle Big Data. In this paper, a framework is proposed for incremental parallel interesting association rule mining algorithm for Big Data. The proposed framework incorporates interestingness measures during the process of mining. The proposed framework works to process the incremental data, which usually comes at different times, the user's important knowledge is explored by processing of new data only, without having to return from scratch. One of the main features of this framework is to consider the user domain knowledge, which is monotonically increased. The model that incorporates the users’ belief during the extraction of patterns is attractive, effective and efficient. The proposed framework is implemented on public datasets as well as it is evaluated based on the interesting results that are found.

Mining Characteristic Patterns for Comparative Music Corpus Analysis

A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of classes in top k pattern mining and in individual top ranked patterns. Characteristic pattern mining is applied to the collection of Native American music by Frances Densmore, and the discovered patterns are shown to be supported by Densmore’s own analyses.