Approach User Informed Particular Schemain Sequential Pattern Analysis

Abstract

The advances modelis enabled the access to textual documents to Internet users all over the world with ease. Sequential patterns are focused theme in data mining. Finding the behavior of sequential pattern are helpful in finding many analyzing applications like predicting next event has been vital. Documents created and distributed on the Internet are ever changing in various forms. Most of existing works is devoted to topic modeling and the evolution of individual topics.Documents created and distributed on the Internet changing many forms. Most of backend works is devoted to topic modeling and the evolution of individual topics.The advances of technology overtime have enabled the access to textual documents to internet users. Sequential patterns have been a focused in data mining. Sequential pattern are helpful in finding many analyzing applications like predicting next event has been vital.A lot of researches of text mining focused on extracting topics from document collections and document streams many probabilistic topic models. To achieve this, a set of algorithms is presented for pre-processing the user contents, generate all STP support values for efficient pattern growth, and selecting user-aware rare sequential topics by using rare pattern domain analysis.