Fuzzy Clustering in Web Mining

Web mining is the use of data mining techniques to automatically discover and extract information from web. Clustering is one of the possible techniques to improve the efficiency in information finding process. Conventional clustering classifies the given data objects into exclusive clusters. However such a partition is insufficient to represent many real situations. Hence a fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows the object to belong to multiple clusters with degree of membership. Web data has fuzzy characteristics, so fuzzy clustering is better suitable for web mining in comparison with conventional clustering. In this paper, we have proposed two algorithms that are Fuzzy c-Means (FCM) and Clustering based on Fuzzy Equivalence Relations which can be used for web page mining and web usage mining. The results obtained from the proposed algorithm are more convincing. The experimental results are carried out on different algorithmic parameters on real data. The analysis is being done by comparing the proposed algorithm with conventional clustering algorithms

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection

The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field