Performance Evaluation of K-Means Algorithm and Enhanced Mid-point based K-Means Algorithm on Mining Frequent Patterns

Pattern and classification of stock data is very important for business development in decision making. Timely prediction of latest upcoming trends is also required in business. Clustering is used to generate groups of related patterns, while association provides a way to get generalized rules of dependent variables. Due to increase in the size and complexity of the data, it is impractical to manually analyze, explore, and understand the data. As a result, useful information is often overlooked. Data mining techniques are best suited for analysis of different types of classification, useful patterns extractions and predictions. The of aim this paper is to evaluate the performance of KMeans and proposed enhanced method of K-Means algorithm with improved initial centers using mid-point method for clustering and apply it on Most Frequent Pattern Mining Algorithm to generate frequent patterns

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