Pattern Mining of Big Data

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OBJECTIVES The objectives of the proposed program are threefold: (1) analyze large-scale multilayer networks for community detection and link prediction tasks using efficient clustering techniques and probabilistic inference mechanisms, (2) design accurate and scalable solutions for big data analytics, and (3) bring new contributions to multidimensional data mining for complex network analysis. We will rely on Formal Concept Analysis, which is a theoretically sound framework for knowledge representation, pattern extraction, and visualization. This means that we will continue bringing new contributions to this field, besides also exploring other prominent approaches from data mining, machine learning and probabilistic inference fields. SCIENTIFIC APPROACH To achieve the first objective, we will design new community detection and evolution algorithms by either combining message passing-based with mixture density-based clustering or by interleaving message passing-based and similarity-based clustering. New probabilistic inference algorithms for link prediction will be designed as well. For the second objective, we will rely on theoretical methods and practical tools from distributed database theory, machine learning, and formal concept analysis to analyze big data problems. This includes data fragmentation and decomposition, parallel algorithm design and implementation, and user-oriented pattern mining. For the third objective, we will first enrich our recent work on triadic concept analysis to further study and bring new contributions to polyadic concept analysis with applications in complex network analysis. SHORT AND LONG TERM OBJECTIVES The short-term objective involves the following research activities: (1) continue our ongoing work on defining new efficient algorithms for mining clusters and association rules in triadic concept analysis, and in data and lattice decomposition, (2) exploit key structural properties that often exist in big (social and biological) networks, to investigate new accurate and scalable probabilistic inference algorithms for reasoning and mining services, (3) develop new efficient clustering techniques for community detection in social networks, and (4) leverage massive data properties to improve inference procedures in learning purposes. The medium-term objective aims at (1) deriving new scalable algorithms for both data clustering and probabilistic inference, and (2) exploring advanced case studies in industry and business. The long-term objective is to build on the results of our short and medium-term objectives to further tackle less trivial issues and develop a robust and flexible environment for the discovery and management of patterns in big data problems.