On defining OLAP formulations

^** Email: vutsinas{at}upatras.gr Recently there has been increasing interest in On Line AnalyticalProcessing (OLAP) to satisfy the organizational needs of high-levelinformation delivery and advanced data analysis. The actualapplication of OLAP tools involves the use of various functions,such as the common drilling down and slicing and dicing. Usuallyeach particular OLAP function is comprehensive and intuitive.However, sophisticated use of OLAP tools requires complicatedcombinations of different OLAP functions that are not straight-forwardfor end users or designers. In this paper we attempt to enumerateand formally define OLAP functions by defining a new OLAP modelthat provides a broader view of OLAP. We demonstrate the expressiveadequacy of the new OLAP model with application examples.     

Machine-part cell formation using biclustering

Cellular manufacturing is the cornerstone of many modern flexible manufacturing techniques, taking advantage of the similarities between parts in order to decrease the complexity of the design and manufacturing life cycle. Part-Machine Grouping (PMG) problem is the key step in cellular manufacturing aiming at grouping parts with similar processing requirements or similar design features into part families and by grouping machines into cells associated to these families. The PMG problem is NP-complete and the different proposed techniques for solving it are based on heuristics. In this paper, a new approach for solving the PMG problem is proposed which is based on biclustering. Biclustering is a methodology where rows and columns of an input data matrix are clustered simultaneously. A bicluster is defined as a submatrix spanned by both a subset of rows and a subset of columns. Although biclustering has been almost exclusively applied to DNA microarray analysis, we present that biclustering can be successfully applied to the PMG problem. We also present empirical results to demonstrate the efficiency and accuracy of the proposed technique with respect to related ones for various formations of the problem.     

CL.E.KMODES: a modified <Emphasis Type="Italic">k</Emphasis>-modes clustering algorithm

In this paper we present a new method for clustering categorical data sets named CL.E.KMODES. The proposed method is a modified k-modes algorithm that incorporates a new four-step dissimilarity measure, which is based on elements of the methodological framework of the ELECTRE I multicriteria method. The four-step dissimilarity measure introduces an alternative and more accurate way of assigning objects to clusters. In particular, it compares each object with each mode, for every attribute that they have in common, and then chooses the most appropriate mode and its corresponding cluster for that object. Seven widely used data sets are tested to verify the robustness of the proposed method in six clustering evaluation measures.