WCOND-mine: algorithm for detecting Web content outliers from Web documents

Outlier mining is dedicated to finding data objects, which differ significantly from the rest of the data. Outlier mining has been extensively studied in statistics and recently data mining. However, exploring the Web for outliers has received very little attention in the mining community. Web content outliers are documents with ´varying contents ´ compared to similar Web documents taken from the same domain. Mining Web content outliers may lead to the identification of competitors and emerging business patterns in electronic commerce. This paper proposes WCOND-mine algorithm for mining Web content outliers using n-grams without a domain dictionary. Experimental results with embedded motifs show that WCOND-mine is capable of finding Web content outliers from Web datasets.