Data Mining and Knowledge Engineering

V. K. Nagappan ¹, M. Selvam Amalraj ², N. Venkatesan ²

Affiliations
1 Department of Information Technology, Bharathiyar College of Engineering and Technology, Karaikal-609609, IN
2 Department of Computer Science and Engineering, Bharathiyar College of Engineering and Technology, Karaikal-609609, IN

Abstract

Data mining software is software that allows you to analyze large volumes of raw data from business systems, applications, databases, web sites, and text based mediums that improve your business processes and increase your business performance. The manual calculation of support for item-sets and calculation of confidence level for association rules extracted were very difficult to do and it takes lot of time. Also they are prone to many errors. The automated Data Mining system implemented in computer will very easily do the difficult job of identifying the combination of items and it gives the support level of item-sets and confidence level of association rules extracted in terms of seconds. Market Basket analysis is possibly the largest application for algorithms that discover Association Rules. In this paper, we discuss about the dataset and its process is done through single dimensional array. Experimental result carries out the performance of the new algorithmic approach.

Keywords

Data Mining, Association Rules, Apriori Algorithm, Single Dimensional Array.

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E. Ramaraj ¹, N. Venkatesan

Affiliations
1 Madurai Kamaraj University, Madurai, Tamilnadu, IN

Abstract

The tremendous growth in data has generated the need for new techniques that can intelligently transform the massive data into useful information and knowledge. Data Mining is such a technique that extracts non-trivial, implicit, previously unknown and potentially useful information from the data in databases. Association Rule Mining is one of the most important and well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations of casual structures among sets of items in the transaction databases or other data repositories. Association rules are widely used in market databases, spatial databases, biological databases, medical databases and crime databases. This paper focuses a new algorithm to mine both positive and negative rules from the real-time surveyed medical database. Association rules are defined as implication of the form A->B where A and B are frequent itemsets in a transaction database. This new algorithm extends this definition to include association rules of forms A ->^B, ^A -> B and ^A -> ^B, which indicate negative associations between itemsets is called negative rules. Negative rules are generated from infrequent itemsets using multidimensional data model.

Keywords

Data Mining, Association Rules, Infrequent Itemsets Negative Rules.

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