Main approaches to corpus-based semantic class mining include distributional similarity (DS) and pattern-based (PB). In this paper, we perform an empirical comparison of them, based on a publicly available dataset containing 500 million web pages, using various categories of queries. We further propose a frequencybased rule to select appropriate approaches for different types of terms.

News web videos exhibit several characteristics, including a limited number of features, noisy text information, and error in near-duplicate key frames (NDK) detection. In this paper, a novel framework is proposed to better group the associated web videos to events. First, the data preprocessing stage performs feature selection and tag relevance learning. Next, multiple correspondence analysis ...

The candidate confirms that the work submitted is her own and that appropriate credit has been given where reference has been made to the work of others i ACKNOWLEDGEMENT I would like to express my gratitude to my supervisor Eric Atwell, Bill Whyte for helpful commentary and suggestions and also to Clive Souter, George Demetriou who provided me with LKB dictionary and gave me good suggestions. ...

In this article we propose a Layered Locality Sensitive Hashing Algorithm to perform similarity search on the web log sequence data. Locality Sensitive Hashing has been found to be an efficient technique for the approximate nearest neighbor search over a large database, as it has sub-linear dependence on the data size even for high dimension. Mining the large web log data to provide customised ...

The similarity of web queries plays an important role in capturing frequently asked questions, most popular topics of search engine or automatic query expansion. Accurate measurement of similarity between queries is crucial. The paper presents a new model for similarity metric of web queries using user logs and applied it into information retrieval for query expansion. Different from previous w...

Clustering web users is one of the most important research topics in web usage mining. Existing approaches cluster web users based on the snapshots of web user sessions. They do not take into account the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by clustering web users based on the evolutions of their historical web sessions. We present an algorith...

Currently, World-Wide Web has developed to a distributed information space with nearly 100 million workstations and several billion pages, which brings the people great trouble in finding needed information although huge amount of information available on webs. The search engine is a very important tool for people to obtain information on Internet, but the low-precision and lowrecall exist wide...

A ubiquitous presence of sequence data across fields, like, web, healthcare, bioinformatics, text mining, etc., has made sequence mining a vital research area. However, sequence mining is particularly challenging because of absence of an accurate and fast approach to find (dis)similarity between sequences. As a measure of (dis)similarity, mainstream data mining methods like k-means, kNN, regres...

Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of high-quality ontologies and encoding fine...