FREQUENT ITEMSET MINING USING ECLAT WITH RELATIVE PROFIT AND PRICE

Data mining is the process of extracting useful information from the huge amount of data stored in the databases. Data mining tools and techniques help to predict business trends those can occur in near future. Many industries are becoming interested in mining such patterns from their databases. The discovery of interesting correlation relationships among huge amounts of business transaction records can help in many business decision-making processes, such as catalog design, cross-marketing, and customer shopping behavior analysis. Eclat is a classical algorithm for mining frequent itemsets, which is based on vertical layout databases. It is greatly different from those algorithms based on horizontal layout databases, such as algorithm Apriori and FP-Growth. Eclat uses vertical data format for frequent pattern mining. It is depth first search technique. It is proved that Eclat is better then apriori algorithm. It needs less database scan compare to apriori. Eclat is faster then apriori. Eclat is not used with any kind of utility. In market basket analyses retailers have to analyze frequent itemset which have high profit corresponding to its price of that itemset. Investment is basic constrained in any business. While purchasing items from company or agency retailers have limited money and they have to invest in varying items with certain quantity by that limited money. While offering package of items retailers has to aware about limit of cost. Therefore, this work investigates working of eclat with utility called “Relative Profit” and with “Price”. The main aim of this research is to look for and manipulate relative profit and price with algorithm to find itemset with high relative profit with price limit.