Data Mining and Knowledge Engineering

M. Banu Priya ¹, V. Umarani ¹

Affiliations
1 Department of Computer Science, Sri Ramakrishna College of Arts & Science for Women, Coimbatore, IN

Abstract

Data Mining is often considered as a process of automatic discovery of new knowledge from large databases. The extensive amount of knowledge and data stored in databases requires the development for storing and accessing the data, data analysis and effective use of stored knowledge of data. Association Rule Mining (ARM) is one of the important aspects in data mining, which generates large amount of itemsets in database. Many algorithms have been proposed to efficiently mine association rules. One of the most important approaches is the frequent pattern growth (FP-growth) method, which is efficient and scalable. In order to improve the efficiency of FP-growth method, this paper is surveyed on association rule mining with the aid of FP-growth algorithm in various aspects.

Keywords

FP-Tree, Frequent Itemset Mining, Association Rule.

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L. Padmavathy ¹, V. Umarani ¹

Affiliations
1 Department of Computer Science, Sri Ramakrishna College of Arts & Science for Women, Coimbatore, IN

Abstract

Association rule mining (ARM) is one of the most important techniques in data mining. The task is to find relationship between presences of various items in a given database. Frequent itemset mining plays an important role in data mining and also used to form association rules. A rule is represented as A=>B, where A and B are individual items in the database. Many business applications focus on discovery of frequent itemsets and association rules in order to improve their strategy. Association rule mining is efficiently improved by using various techniques. One of the widely used techniques among them is Hashing technique. Hashing technique utilizes hash tables to store itemsets and reduce the complexity of deriving association rules from large databases. This paper focus on how to improve the efficiency of association rules based on hashing technique.

Keywords

Association Rules, Frequent Itemset, Hashing, and Collisions.

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