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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 1 Issue 3, December 2007

Introduction to special issue ACM SIGKDD 2006
Roberto Bayardop, Johannes Gehrke, Kristin P. Bennett, Gautam Das, Dimitrios Gunopulos
Article No.: 9
DOI: 10.1145/1297332.1297333

RIC: Parameter-free noise-robust clustering
Christian Böhm, Christos Faloutsos, Jia-Yu Pan, Claudia Plant
Article No.: 10
DOI: 10.1145/1297332.1297334

How do we find a natural clustering of a real-world point set which contains an unknown number of clusters with different shapes, and which may be contaminated by noise? As most clustering algorithms were designed with certain...

Semantic annotation of frequent patterns
Qiaozhu Mei, Dong Xin, Hong Cheng, Jiawei Han, Chengxiang Zhai
Article No.: 11
DOI: 10.1145/1297332.1297335

Using frequent patterns to analyze data has been one of the fundamental approaches in many data mining applications. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of...

Measuring and extracting proximity graphs in networks
Yehuda Koren, Stephen C. North, Chris Volinsky
Article No.: 12
DOI: 10.1145/1297332.1297336

Measuring distance or some other form of proximity between objects is a standard data mining tool. Connection subgraphs were recently proposed as a way to demonstrate proximity between nodes in networks. We propose a new way of measuring and...

Learning to detect events with Markov-modulated poisson processes
Alexander Ihler, Jon Hutchins, Padhraic Smyth
Article No.: 13
DOI: 10.1145/1297332.1297337

Time-series of count data occur in many different contexts, including Internet navigation logs, freeway traffic monitoring, and security logs associated with buildings. In this article we describe a framework for detecting anomalous events in such...

Assessing data mining results via swap randomization
Aristides Gionis, Heikki Mannila, Taneli Mielikäinen, Panayiotis Tsaparas
Article No.: 14
DOI: 10.1145/1297332.1297338

The problem of assessing the significance of data mining results on high-dimensional 0--1 datasets has been studied extensively in the literature. For problems such as mining frequent sets and finding correlations, significance testing can be done...