We describe a method for sparse feature selection for a class of problems motivated by our work in Computer-Aided Detection (CAD) systems for identifying structures of interest in medical images. Typical CAD data sets for classification are large (several thousand candidates) and unbalanced (significantly fewer than 1% of the candidates are ”positive”). To be accepted by physicians, CAD systems...

Methylated and un-methylated control DNA is an important part of DNA methylation studies. Although human and mouse DNA methylation control sets are commercially available, in case of methylation studies on other species such as animals, plants, and bacteria, control sets need to be prepared. In this paper a simple method of generating methylated and un-methylated control DNA is described. Whole...

Existing parallel algorithms for association rule mining have a large inter-site communication cost or require a large amount of space to maintain the local support counts of a large number of candidate sets. This study proposes a de-clustering approach for distributed architectures, which eliminates the inter-site communication cost, for most of the influential association rule mining algorith...

We propose an efficient technique for mining frequent itemsets in large databases making use of Optical Neural Network Model. It eliminates the need to generate candidate sets and joining them for finding frequent itemsets for association rule mining. Since optical neural network performs many computations simultaneously, the time complexity is very low as compared to other data mining techniqu...

Large sets of combinatorial designs has always been a fascinating topic in design theory. These form partition the whole space into with same parameters. In particular, large set block designs, whose blocks are size k taken from an n-set, is all k-subsets n-set disjoint copies defined on and The current most intriguing question this direction whether Steiner quadruple systems exist to provide e...

Aiming to these present frequent neighboring class set mining algorithms existing more repeated computing and redundancy neighboring class set, this paper proposes an algorithm of alternately mining frequent neighboring class set, which is suitable for mining frequent neighboring class set of objects in large spatial data. The algorithm uses the regression method to create database of neighbori...