Pattern Discovery for Multiple Data Sources Based on Item Rank

Retail company’s data may be geographically spread in different locations due to huge amount of data and rapid growth in transactions. But for decision making, knowledge workers need integrated data of all sites. Therefore the main challenge is to get generalized patterns or knowledge from the transactional data which is spread at various locations. Transporting data from those locations to server site increases the cost of transportation of data and at the same time finding patterns from huge data on the server increases the time and space complexity. Thus multi-database mining plays a vital role to extract knowledge from different data sources. Thus the technique proposed finds the patterns on various sites and instead of transporting the data, only the patterns from various locations get transported to the server to find final deliverable pattern. The technique uses the ranking algorithm to rank the items based on their profit, date of expiry and stock available at each location. Then association rule mining (ARM) is used to extract patterns based on ranking of items. Finally all the patterns discovered from various locations are merged using pattern merger algorithm. Proposed algorithm is implemented and experimental results are taken for both classical association rule mining on integrated data and for datasets at various sources. Finally all patterns are combined to discover actionable patterns using pattern merger algorithm given in section V.