Asian Journal of Engineering and Technology Innovation

Minimum Consistent Subset Cover Problem : A Minimization view Of Data Mining

Madhugandha Bhosale

Researcher

Abstract

In this paper, we tend to introduce and study the minimum consistent set cowl (MCSC) drawback. Given a finite ground set and a constraint t, notice the minimum variety of consistent subsets that cowl X, wherever a set of X is consistent if it satisfies t. The MCSC drawback generalizes the normal set covering drawback and has minimum pack partition (MCP),a twin drawback of graph coloring, as Associate in Nursing instance. several common data processing tasks in rule learning, clustering, and pattern mining are often developed as MCSC instances. Especially, we tend to discuss the minimum rule set (MRS) drawback that minimizes model complexness of call rules, the converse k-clustering drawback that minimizes the quantity of clusters, and also the pattern report drawback that minimizes the number of patterns. For any of those MCSC instances, our planned generic formula CAG are often directly applicable. CAG starts by constructing a greatest optimum partial answer, then performs Associate in Nursing example-driven specific-to-general search on a dynamically maintained bipartite assignment graph to at the same time learn a collection of consistent subsets with little cardinality covering the bottom set.

Keywords:

Minimum consistent set cowl, set covering, graph coloring, minimum pack partition, minimum star partition, minimum rule set, converse k-clustering, pattern report