Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining freq...

In this paper, we approach the classical problem of clustering using solution concepts from cooperative game theory such as Nucleolus and Shapley value. We formulate the problem of clustering as a characteristic form game and develop a novel algorithm DRAC (Density-Restricted Agglomerative Clustering) for clustering. With extensive experimentation on standard data sets, we compare the performan...

A matrix M over a fixed alphabet is k-anonymous if every row in M has at least k − 1 identical copies in M . Making a matrix kanonymous by replacing a minimum number of entries with an additional ?-symbol (called “suppressing entries”) is known to be NP-hard. This task arises in the context of privacy-preserving publishing. We propose and analyze the computational complexity of an enhanced anon...

Modern search methods for optimization consider hybrid search metaheuristics those employing general optimizers working together with a problem-specific local search procedure. The hybridism comes from the balancing of global and local search procedures. A challenge in such algorithms is to discover efficient strategies to cover all the search space, applying local search only in actually promi...

The n o t i o n of * concept* based on func t i o n a l i t y of ob jec ts is de f ined and made use o f in the context o f p a t t e r n c l u s t e r i ng. An approach to p a r t i t i o n ob jec ts us ing a knowledge base is presented. A d i f f e r e n t c lass o f concepts c a l l e d conceptual t r a n s formers is proposed and i t s e f f e c t s on c l u s t e r i n g i s looked i n t o .

The Logical Analysis of Data (LAD) is a combinatorics, optimization and logic based methodology for the analysis of datasets with binary or numerical input variables, and binary outcomes. It has been established in previous studies that LAD provides a competitive classification tool comparable in efficiency with the top classification techniques available. The goal of this paper is to show that...

The recent advances of genome-scale sequencing and array technologies have made it possible to monitor simultaneously the expression pattern of thousands or tens of thousands of genes. One of the following steps is to discover or extract the information for the genetic networks by analyzing such massive data sets. Therefore, various clustering methods, such as hierarchical clustering [3] or sel...

This study focused on development and application of efficient algorithm for clustering and classification of supervised visual data. In machine learning clustering classification and clustering is most useful techniques in pattern recognition and computer vision. Existing techniques for subspace clustering makes use of rank minimization and spars based that are computationally expensive and ma...

Clustering genes into biological meaningful groups according to their pattern of expression is a main technique of microarray data analysis, based on the assumption that similarity in gene expression implies some form of regulatory or functional similarity. We give an overview of various clustering techniques, including conventional clustering methods (such as hierarchical clustering, k-means c...