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Knowledge Discovery from Data (TKDD)

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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 6 Issue 1, March 2012

From Context to Distance: Learning Dissimilarity for Categorical Data Clustering
Dino Ienco, Ruggero G. Pensa, Rosa Meo
Article No.: 1
DOI: 10.1145/2133360.2133361

Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical attribute, since the values are not...

Efficient Mining of Gap-Constrained Subsequences and Its Various Applications
Chun Li, Qingyan Yang, Jianyong Wang, Ming Li
Article No.: 2
DOI: 10.1145/2133360.2133362

Mining frequent subsequence patterns is a typical data-mining problem and various efficient sequential pattern mining algorithms have been proposed. In many application domains (e.g., biology), the frequent subsequences confined by the predefined...

Isolation-Based Anomaly Detection
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
Article No.: 3
DOI: 10.1145/2133360.2133363

Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which...

A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
Yu Jin, Nick Duffield, Jeffrey Erman, Patrick Haffner, Subhabrata Sen, Zhi-Li Zhang
Article No.: 4
DOI: 10.1145/2133360.2133364

The ability to accurately and scalably classify network traffic is of critical importance to a wide range of management tasks of large networks, such as tier-1 ISP networks and global enterprise networks. Guided by the practical constraints and...