MARKET BASKET ANALYSIS FOR DATA MINING: concepts and techniques
International Journal of Latest Research in Engineering and Technology
Data mining (DM), also called Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns using specific DM technique. The efficient discovery of previously unknown, valid, potentially useful, understandable patterns in large datasets. The analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. Association rule
... ing represents a data mining technique and its goal is to find interesting association or correlation relationships among a large set of data items. With massive amounts of data continuously being collected and stored in databases, many companies are becoming interested in mining association rules from their databases to increase their profits. For example, the discovery of interesting association relationships among huge amounts of business transaction records can help catalog design, cross marketing, loss leader analysis, and other business decision making processes. If/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository A typical example of association rule mining is market basket analysis. This process analyzes customer buying habits by finding associations between the different items that customers place in their "shopping baskets" using confidence and support factors.