User Behaviour Pattern Mining from Weblog

Abstract

In this paper, the authors build a tree using both frequent as well as non-frequent items and named as Revised PLWAP with Non-frequent Items RePLNI-tree in single scan. While mining sequential patterns, the links related to the non-frequent items are virtually discarded. Hence, it is not required to delete or maintain the information of nodes while revising the tree for mining updated weblog. It is not required to reconstruct the tree from scratch and re-compute the patterns each time, while weblog is updated or minimum support changed, since the algorithm supports both incremental and interactive mining. The performance of the proposed tree is better, even the size of incremental database is more than 50% of existing one, while it is not so in recently proposed algorithm. For evaluation purpose, the authors have used the benchmark weblog and found that the performance of proposed tree is encouraging compared to some of the recently proposed approaches.

Big Data Clustering Techniques Challenged and Perspectives: Review

“…The agglomerative hierarchical approaches begin with considering the individual point in data as a cluster, then continually fusing the most similar clusters until only one remains at the end. Two algorithms, BIRCH [54] and CHAMELEON, take advantage of this concept [16]. A divisive hierarchical clustering approach treats the entire data set as a single large cluster, then splits the most relevant clusters at each stage until a user-defined threshold of clusters is reached.…”

“…It is critical to employ advanced knowledge discovery tools to deal with this flood of data. Data mining techniques [68] [54] are excellent information-seeking tools for this purpose. One of them is Clustering, which is described as "a strategy for dividing data into groups in such a way that items in one group have more similarity than objects in other groups" [31].…”

Towards Comparative Mining of Web Document Objects with NFA

“…Borges and Leven (1999) categorized web mining into three areas: web structure mining, web usage mining and web content mining. Web usage mining processes usage information or the history of user's visit to different web pages, which are generally stored in chronological order in web log file, server log, error log and cookie log (Buchner & Mulvenna, 1998;Ezeife & Lui, 2009;Priya & Vadivel, 2012).When any mechanism is used to extract relevant and important information from web documents or to discover knowledge or pattern from web documents, it is then called web content mining. Traditional mechanisms include: providing a language to extract certain pattern from web pages, discovering frequent patterns, clustering for document classification, machine learning for wrapper (e.g., data extraction program) induction, and automatic wrapper generation (Liu & Chen-Chung-Chang, 2004;Muslea, Minton, & Knoblock, 1999;Zhao et al, 2005;Crescenzi, Mecca, & Merialdo, 2001;Liu, 2007).…”