Topical trends in association rule mining

Knowledge Discovery is a key practice in data analysis, data mining, machine learning and artificial intelligence. Data minigg as a part of the knowledge discovery process offers a new approach to visualize data, the real worth which lies in productively seeking out trends within data and to provide this perceptive to organizations that maintain substantial amounts of Information and can use this understanding in making strategic decisions in different fields of endeavor.

Technically, Data Mining consists of the nontrivial extraction of implicit, previously unknown, and potentially useful information from data² The maturity of this technology has further led to the diversification of data mining into diverse techniques varying in the principle, type or data to be mined. A typical and popular way of knowledge representation in data mining, association rule mining aims at extracting interesting correlations, frequent patterns, associations or casual structure sets of items in the transaction databases or other data repositories, Associaation rules have been extensively used in varied domains. In this paper we discuss the scope of a few recent and contemporary applications of association rule mining in the form of association rule network, heterogeneous association rules and rough association rule mining.