Spatio-Temporal Frequent Itemset Mining on Web Data

Web generates enormous volumes of spatiotemporal data every second. Such data includes transactional data on which association rule mining can be performed. Applications includes fraud detection, consumer purchase pattern identification, recommendation systems etc. Essence of spatiotemporal information alongwith the transactional data comes from the fact that the association rules or frequent patterns in the transactions are highly determined by the location and time of the occurrence of that transaction. For example, customer purchase of product depends upon the season and location of buying that product. To extract frequent patterns from such large databases, most existing algorithms demands enormous amounts of resources. The present work proposes a spatiotemporal association rule mining algorithm using hashing, to facilitate reduced memory access time and storage space. Hash based search technique is used to fasten the memory access by directly accessing the required spatio-temporal information from the schema. There are a numerous hash based search techniques that can be used. But to reduce collision, direct address hashing is focused upon primarily in this work. However, in future we plan to extend our results over different search techniques. Our results are compared with exiting Spatio-Temporal Apriori algorithm, which is one of the established association rule mining algorithm. Furthermore, experiments are demonstrated on several synthetically generated and web based datasets. Subsequently, a comparison over different datasets is given. Our algorithm shows improved results when evaluated over several metrics such as support of frequent itemsets and percentage gain in reduced memory access time. In future we plan to extend this work to various benchmark datasets.