An Efficient Approach for Incremental Association Rule Mining through Histogram Matching Technique

2. Preliminary Definitions

Data mining is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories and it is the core process of Knowledge Discovery in Database (KDD) (Zhao & Bhowmick, 2003). Data mining techniques include association rule mining, classification, clustering, mining time series, and sequential pattern mining, to name a few, with association rules mining receiving a significant research attention (Huang, Dai, & Chen, 2007).

Let at any given instant of time ‘t’ there is a transaction dataset D. Here D consists of a set of transactions and each transaction is identified with a transaction identifier, TID. Now let us assume that at time ‘t+l’, the database is updated such that a set of transaction D⁺ is added to the original database D and a set of transaction D^- is deleted from the dataset. So the resulting modified database D* can be represented as: D* = (D U D⁺) – D^-_.