On mining complex sequential data by means of FCA and pattern structures

Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work. Keywords: data mining; formal concept analysis; pattern structures; projections; sequences; sequential data.Comment: An accepted publication in International Journal of General Systems. The paper is created in the wake of the conference on Concept Lattice and their Applications (CLA'2013). 27 pages, 9 figures, 3 table

The representation of sequential patterns and their projections within Formal Concept Analysis

International audienceNowadays data sets are available in very complex and heterogeneous ways. The mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using an elegant mathematical framework: Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patients' patterns from a French healthcare data set of cancer patients. The quantitative and qualitative results are reported in this use case which is the main motivation for this work

RCA-Seq: an Original Approach for Enhancing the Analysis of Sequential Data Based on Hierarchies of Multilevel Closed Partially-Ordered Patterns

International audienceMethods for analysing sequential data generally produce a huge number of sequential patterns that have then to be evaluated and interpreted by domain experts. To diminish this number and thus the difficulty of the interpretation task, methods that directly extract a more compact representation of sequential patterns, namely closed partially-ordered patterns (CPO-patterns), were introduced. In spite of the fewer number of obtained CPO-patterns, their analysis is still a challenging task for experts since they are unorgan-ised and besides, do not provide a global view of the discovered regularities. To address these problems, we present and formalise an original approach within the framework of Relational Concept Analysis (RCA), referred to as RCA-Seq, that focuses on facilitating the interpretation task of experts. The hierarchical RCA result allows to directly obtain and organize the relationships between the extracted CPO-patterns. Moreover, a generalisation order on items is also revealed, and multilevel CPO-patterns are obtained. Therefore, a hierarchy of such CPO-patterns guides the interpretation task, helps experts in better understanding the extracted patterns, and minimises the chance of overlooking interesting CPO-patterns. RCA-Seq is compared with another approach that relies on pattern structures. In addition, we highlight the adaptability of RCA-Seq by integrating a user-defined tax-* onomy over the items, and by considering user-specified constraints on the order relations on itemsets

Numerical Pattern Mining Through Compression

International audiencePattern Mining (PM) has a prominent place in Data Science and finds its application in a wide range of domains. To avoid the exponential explosion of patterns different methods have been proposed. They are based on assumptions on interestingness and usually return very different pattern sets. In this paper we propose to use a compression-based objective as a well-justified and robust interestingness measure. We define the description lengths for datasets and use the Minimum Description Length principle (MDL) to find patterns that ensure the best compression. Our experiments show that the application of MDL to numerical data provides a small and characteristic subsets of patterns describing data in a compact way

LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Jitender Deogu

The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems