MPGN – An Approach for Discovering Class Association Rules

his article presents some of the results of the Ph.D. thesis Class Association Rule Mining Using MultiDimensional Numbered Information Spaces by Iliya Mitov (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt University, Faculty of Science on 15 November 2011 in Belgium

Test, Teachers, Quorum (Pure Populations)

The “trial and error” method is fundamental for Master Minddecision algorithms. On the basis of Master Mind games and strategies weconsider some data mining methods for tests using students as teachers.Voting, twins, opposite, simulate and observer methods are investigated.For a pure data base these combinatorial algorithms are faster then manyAI and Master Mind methods. The complexities of these algorithms arecompared with basic combinatorial methods in AI. ACM Computing Classification System (1998): F.3.2, G.2.1, H.2.1, H.2.8, I.2.6.

Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.

An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database

Sequential pattern mining is an important subject in data mining with broad applications in many different areas. However, previous sequential mining algorithms mostly aimed to calculate the number of occurrences (the support) without regard to the degree of importance of different data items. In this paper, we propose to explore the search space of subsequences with normalized weights. We are not only interested in the number of occurrences of the sequences (supports of sequences), but also concerned about importance of sequences (weights). When generating subsequence candidates we use both the support and the weight of the candidates while maintaining the downward closure property of these patterns which allows to accelerate the process of candidate generation.