In the article“Web Mining: Data Mining on Web” 3 different components of Web have been described and mainly Usage has discussed in detail analyzed with examples. The general theme article is clarified by giving such sub-topics: Difficulties analyzing data from Internet, Stages Web-Mining, Analysis use resources (Web Mining), Server Log Files etc. General Relationships Between Categories Tasks a...

The main objective of data mining is to acquire information from a set of data for prospect applications using a measure. The concerning issue is that one often has to deal with large scale data. Several dimensionality reduction techniques like various feature extraction methods have been developed to resolve the issue. However, the geometric view of the applied measure, as an additional consid...

Web is a vast area for data mining research. It is used in finding the user access patterns from web access log. User page visits are sequential in nature In this paper,I proposed a new clustering algorithm, SeqDBSCAN for clustering sequential data.. we adopted a similarity preserving function called sequence and set similarity measure SM that captures both the order of occurrence of page visit...

SubSift matches submitted conference or journal papers to potential peer reviewers based on the similarity between the paper’s abstract and the reviewer’s publications as found in online bibliographic databases such as Google Scholar. Using concepts from information retrieval including a bag-of-words representation and cosine similarity, the SubSift tools were originally created to streamline t...

Mining Image data is one of the essential features in the present scenario. Image data is the major one which plays vital role in every aspect of the systems like business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. The other area in the Image mining system is the Content-Based Image Retrieval (CBIR). CBIR systems perform retrieval based on ...

Clustering on Distribution measurement is an essential task in mining methodology. The previous methods extend traditional partitioning based clustering methods like k-means and density based clustering methods like DBSCAN rely on geometric measurements between objects. The probability distributions have not been considered in measuring distance similarity between objects. In this paper, object...

Given that pairwise similarity computations are essential in ontology learning and data mining, we propose a similarity framework that is based on a conventional Web search engine. There are two main aspects that we can benefit from utilizing a Web search engine. First, we can obtain the freshest content for each term that represents the upto-date knowledge on the term. This is particularly use...