SAT-Based Data Mining

Several propositional satisfiability (SAT) based encodings have been proposed to deal with various data mining problems including itemset and sequence mining problems. This research issue allows to model data mining problems in a declarative way, while exploiting efficient SAT solving techniques. In this paper, we overview our contributions on the application of propositional satisfiability (SAT) to model and solve itemset mining tasks. We first present a SAT based encoding of frequent closed itemset mining task as a propositional formula whose models corresponds to the patterns to be mined. Secondly, we show that some data mining constraints can be avoided by reformulation. We illustrate this issue by reformulating the closeness constraint using the notion of minimal models. Finally, we also addressed the scalability issue, one of the most important challenge of these nice declarative framework. To this end, we proposed a complete partition based approach whose aim is to avoid encoding the whole database as a single formula. Using a partition on the set of items, our new approach leads to several propositional formulas of reasonable size. The experimental evaluation on several known datasets shows huge improvements in comparison to the direct approach without partitioning, while reducing significantly the performance gap with respect to specialized algorithms.

Multiobjective Adaptive Fuzzy C-Means Algorithm for Mining Frequent Closed Itemset: A Lattice Based Approach for Time Series Data

Of late, frequent itemset mining from the data bases has emerged as a highly challenging task. And, there is a feast of various approaches launched with an eye on extracting the frequent rules from the database. In the erstwhile frequent itemset mining technique, the frequent itemset are extracted from the time series data by means of Adaptive Fuzzy C-Means (AFCM) clustering, though it is saddled with several setbacks in the clustering procedure. This arises on account of the fact that in clustering, the number of itemset is mammoth, and hence the method consumes a lot of time in addition to spending enhanced scanning time. Therefore, with a view tackling these adverse features, an innovative frequent itemset mining method is suggested in this document. At the outset, the time sequence database values are grouped by means of the Multiobjective AFCM method. Subsequently, the frequent itemset are extracted from the grouped outcomes by employing the lattice based method. The novel technique is performed in the MATLAB platform and its excellence is appraised by means of rainfall database.

All in One: Mining Multiple Movement Patterns

Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches.

Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner. The proposed algorithm has been implemented in the commonly used MATLAB environment and freely available for researchers.