WEB INTERACTION MINING USING PENTA LAYERED ARTIFICIAL NEURAL NETWORK CLASSIFIER
Siamaladevi S, Kavinkumar M, Harirajan K, Chanthirahari I · International Journal of Computer Science Engineering and Technology · 2018
Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. A sequence database is a set of ordered elements or events, stored with or without a concrete notion of time. Each itemset contains a set of items which include the same transaction-time value. While association rules indicate intra-transaction relationships, sequential patterns represent the correlation between transactions. Sequential pattern mining (SPM) is the process that extracts certain sequential patterns whose support exceeds a predefined minimal support threshold. In this paper we propose to identifying the right pattern granularity for both sequential pattern mining and modeling, we have successful applications in B2B (Business-to-Business) marketing analytics, healthcare operation and management, and modeling of the product adoption in digit markets, as three case studies in dynamic business environments. The advantage of proposed algorithm is that it dosen’t need to generate conditional pattern bases and sub- conditional pattern tree recursively. And the results of the experiments show that it works faster than previous algorithms.