Mining of Datasets with an Enhanced Apriori Algorithm

Khairuddin Khairuddin

doi:10.3844/jcssp.2012.599.605

Abstract

Problem statement: Classical association rules are mostly mining intra-transaction associations i.e., associations among items within the same transaction where the idea behind the transaction could be the items bought by the same customer on the same day. The goal of inter-transaction association rules is to represent the associations between various events found in different transactions. Approach: In this study, we break the barrier of transactions and extend the scope of mining association rules from traditional single-dimensional, intratransaction associations to N-Dimensional, inter-transaction associations. With the introduction of dimensional attributes, we lose the luxury of simple representational form of the classical association rules. Mining inter-transaction associations pose more challenges on efficient processing than mining intra-transaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken. Results: Various tests also conducted using the data set collected from different Stock Exchange (SE).Various experimental results are reported by comparing with real life and synthetic datasets and we show the effectiveness of our work in generating rules and in finding acceptable set of rules under varying conditions. Conclusion/Recommendations: This study introduce the notion of N-Dimensional inter-transaction association rule, define its measurements: support and confidence and develop an efficient algorithm called Modified Apriori.

Highlights

Among all the data mining problems, discovering association rules from large databases is probably the most significant contribution from the database community to the field (Agrawal et al, 1993; Agrawal and Srikant, 1994; Dong and Han, 2007; Feng et al, 2002; Han and Fu, 1995; Kamber et al, 1997; Shankar et al, 2009)
Based on what have been described above, we propose N-dimensional inter-transaction association rules with the classical association rules as a special case
With the introduction of dimensional attributes, we lose the luxury of simple representational form of the classical association rules

Summary

INTRODUCTION

Among all the data mining problems, discovering association rules from large databases is probably the most significant contribution from the database community to the field (Agrawal et al, 1993; Agrawal and Srikant, 1994; Dong and Han, 2007; Feng et al, 2002; Han and Fu, 1995; Kamber et al, 1997; Shankar et al, 2009). Definition 6: An inter-transaction association rule is an implication of the form X ==> Y, where (1) X and Y are sets of event instances in the form of ei(di1,di2,...,din) where (di1,di2,...,din)is the address of ei relative to EBASE(X U Y),ie.,(d01,d02,....,d0n);(2) ei ε E, d0k ε Dom(Dk),(dik+d0k) ε Dom(Dk)(1 ≤iI ≤ u,1 ≤ k ≤ n ) and (3) X ∩ Y = Φ. To investigate the feasibility of mining inter-transaction rules, we implemented two algorithms by extending the Apriori-based algorithm to mine 1-dimensional intertransaction association rules and applied it to the problem of stock price movement prediction. The mining process of n-dimensional intertransaction rules can be divided into three phases: data preparation, Frequent-item set discovery and candidate generation.

MATERIALS AND METHODS

RESULTS AND DISCUSSION

Findings

CONCLUSION