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- W2887706422 abstract "Web robots are scripts that automatically surf the Web’s server structure to locate and index information. These robots are sometimes used maliciously to create a myriad of problems in the functioning of servers. Such automated programs are difficult to trace and triangulate as they mask their identities. A weblog file which comprises of server requests can be used for identifying these robots by using clustering techniques. These log files contain a massive amount of data, and large data clustering algorithms are used to partition the requests into robotic sessions or human sessions. In this paper, a study is conducted, comparing the primary large clustering techniques. For clustering of the HTTP requests, we implemented BIRCH—Balanced Iterative Reducing and Clustering using Hierarchy (Hierarchical clustering technique), DBSCAN—Density-Based Spatial Clustering of Applications with Noise (Density-based clustering technique) and CLIQUE—Clustering in Quest (Grid-based method) using open-source ELKI & JBIRCH java packages. The performances of the three algorithms are compared using internal validating measures -Dunn’s Index, DB Index, and Average Silhouette Index. As a result of the study, we found the optimal number of clusters to be four that produces the best validation measures." @default.
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- W2887706422 date "2018-08-08" @default.
- W2887706422 modified "2023-09-23" @default.
- W2887706422 title "Performance Evaluation of Large Data Clustering Techniques on Web Robot Session Data" @default.
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- W2887706422 doi "https://doi.org/10.1007/978-981-13-0923-6_47" @default.
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