The trend of shopping in current scenario has changed a lot, with the evolution of online shopping. Companies can thrive their business by maintaining robust relation with their customers, by keeping information of consumer behaviour to provide them personalised services. This can be done by data pre-processing which involves extracting the database about the needs of customer’s pattern (i.e. quality, quantity, price and item), followed by data transformation and data mining techniques. But, to extract the discovered patterns in a huge database is still a tedious task, especially in the field of text mining. This paper unfolds various data mining techniques (clustering, classification, decision trees) to discover useful patterns for improving customer’s database accuracy and efficiency taking into consideration, their past performance. Further, we try to present an efficient pattern discovery technique by optimizing the original K-means algorithm, which would perform better in global searching and finding the relevant information.

Data mining, Literature review, clustering, K-Means

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