To Enhancement the Click Stream of Website using GRC Constraints in Web Personalize Clustering Approach

EasyChair Preprint 2370

Abstract

In the current trends every organization manages work and its data online. Even though e-Commerce website maintaining data online in a distributed form. Online approach is very useful to interact with consumer and seller without any dependency of place and time. Every consumer can select product with any brand without wait for a time and produce the order for purchasing. Most of purchasing the product is done by using the website that produce some navigational or access pattern. This access pattern is used to produce some access rules. The proposed Constraint based Closed Sequential Pattern Mining using Self-Organizing Map Clustering (CBCSPMSC) approach first use some profile and GRC constraints for filtration of data between the duration and occurrence of item gap. Now applying closed pattern technique for minimizes the number of rules generation and execution time. At last SOM clustering technique is applied so that every item belong the cluster for partial database scan not whole data with less execution time

Keyphrases: Data Mining, NN-SOM Clustering, Personalization, Web Usage Mining, closed pattern, compactness, data stream, sequential pattern mining

@booklet{EasyChair:2370,
  author    = {Ganga Singh and Harsh Pratap Singh and Kailash Patidar},
  title     = {To Enhancement the Click Stream of Website using GRC Constraints in Web Personalize Clustering Approach},
  howpublished = {EasyChair Preprint 2370},
  year      = {EasyChair, 2020}}