SSM-DBSCANand SSM-OPTICS : Incorporating a New Similarity Measure for Density Based Clustering of Web Usage Data

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Provided by: Engg Journals Publications
Topic: Data Management
Format: PDF
Clustering web sessions is to group web sessions based on similarity and consists of minimizing the intra-group similarity and maximizing the inter-group similarity. Here in this paper, the authors developed a new similarity measure named SSM (Sequence Similarity Measure) and enhanced an existing DBSCAN and OPTICS clustering techniques namely SSM-DBSCAN, and SSM-OPTICS for clustering web sessions for web personalization. Then they adopted various similarity measures like Euclidean distance, Jaccard, cosine and fuzzy similarity measures to measure the similarity of web sessions using sequence alignment to determine learning behaviors of web usage data.
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