Cluster analysis divides data into meaningful or useful groups (clusters). One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behaviour for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. We proposed clustering techniques that are being used in Data Mining is presented. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms with k-means method is one of the clustering techniques. Data mining facilitates marketing sector by classifying customer demographic that can be used to predict which customer will respond to a mailing or buy a particular product and it is very much helpful in growth of business. If meaningful clusters are the goal, then the resulting clusters should capture the "natural" structure of the data. For example, cluster analysis has been used to group related documents for browsing, to find genes.

Cluster analysis divides data into meaningful or useful groups (clusters). One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. Clustering techniques that are being used in Data Mining is presented. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms with k-means method is one of the clustering techniques. Data mining facilitates marketing sector by classifying customer demographic that can be used to predict which customer will respond to a mailing or buy a particular product and it is very much helpful in growth of business. K means method proposed that will improved in marketing sector and also discuss how to support clustering technique in marketing sector. Experimental results show difference between clustered and non clustered data of marketing product that represent in graphically and theoretically. These results help customer to choose the products and also it saves the time. KEYWORD Marketing, data mining, clustering technique. I.