The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation of data has taken place at an explosive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-sale or remote sensing devices has contributed to this explosion of available data. Data confidentiality is a major concern in database systems especially when are huge amounts of data to be processed, so we try to implement a system where we could preserve security and maintain data confidentiality. Further there is a huge trend today towards distributed databases which make data mining very easy, reliable and efficient. In our paper we implement Fast Distributed Mining of Apriori algorithm for mining this huge transactional dataset. With the help of this algorithm we find the frequent item sets that are consumed in all transaction. Finding this frequent item sets in very important. By knowing this frequent item sets one can understand the interest of consumers and focus in profiting his business by mining association rules from them. In other words association rules can be used for decision making. © Research India Publications.
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B. Sreevidya, “An enhanced and productive technique for privacy preserving mining of association rules from horizontal distributed database”, International Journal of Applied Engineering Research, vol. 10, pp. 39126-39130, 2015.