Muhammad Jaminur Rahman et al.

mining, such as knowledge mining from datab haeology, and data dredging.

ases, knowledge extraction, data/ pattern analysis, data arc ining syno 2]. Altern for tively other ata m pular ng is ss of know dous am ledge d of overy in collected a databas nd s.

stored Many people treat data m as a nym an po ly used term, Knowledge Discovery in Databases, or KDD [ a , d ini also treated simply as an essential step in the proce isc e The fast-growing, tremen ount data, in large and numerous databases, has far exceeded our human ability for comprehension without powerful tools [4]. In such situation we become ich rm e . Important decisions are often made based not on ation-rich data stored in databases but rather on a decision maker's intuition, simply because t isio e n the tools to extract the valuable knowledge embedded in the vast am current expert system technologies rely on users or domain experts to manually input knowledge into knowledge bases data r the inform but info ation poor. Cons quently he dec n mak ounts of r does data. In addition, ot have
[5]. Unfortunately, this procedure is prone to biases and errors, and is extremely time consuming and costly. In such situation data mining tools can perform data analysis and may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific or medical research.

## 2.2 Association Rule

Association rule mining finds interesting association or correlation relationships among a large set of data items. In short association rule is based on associated relationships. The discovery of interesting association relationships among huge amounts of transaction records can help in many decision-making processes. Association rules are generated on the basis of two important terms nam Let us consi ns to represent the as r ms. Let D the task database transactions where each transaction T is a set of items such that T J. Each transaction is associated with an identifier, called TID. Let A be a set of items. A transaction T is said to contain A if and only if A T. An association rule is an implication of the form A B,
where A J, B J, and AŀB = ĭ. The rule A B holds in the transaction set D with support s, where s is the percentage of transactions in D that contain A B (i.e. both A and B). This is taken to be the probability, P (A B). The rule A B has confidence c in the transaction set d if c is the percentage of transaction in D containing A that also contain B. This is taken to be the ility, P (B | A). That is, ely minimum support threshold and minimum confidence threshold.

der the following assumptio sociation rule in terms of mathematical epresentation, J = {i1, i2, . . . , im} be a set of ite relevant data, be a set of 

## Upport (A Conditional Probab S B) = P (A B) And Confidence (A B) = P (B | A)

Association Rules that satisfy both a minimum support threshold and minimum confidence d are called strong association rules. A set of items is referred to as an ite mining research literature, "itemset" is more commonly used than "item set". An itemset that ains k items is a k-itemset. The occurrence frequency of an itemset is the number of sactions that contain the itemset. This is also known, simply as the fre or count of the itemset. An itemset satisfies minimum support if the occurrence frequency of the itemset is greater than or equal to the product of minimum support and the total number of sactions in D. The number of transactions required for the itemset to support is therefore referred to as the minimum support count. If an itemset satisfies minimum ort, then it is a frequent itemset. The set of frequent k-itemsets is commonly denoted by Lk.

threshol mset. In data cont tran quency, support count, tran satisfy minimum supp Asso h includes:
ciation rule mining is a two-step process, whic