Introduction to Data Mining

By P. Tan, Michael S. Steinbach, Vipin Kumar. 2005

650 citations recorded by the source corpus.

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In Information Systems

Citations in the corpus

Evaluation of clustering algorithms for financial risk analysis using MCDM methods (2014, neutral): “It measures the similarity between objects and is defined as [67]: Jaccard index 1⁄4 f11 f01 þ f10 þ f11 where f01 is the number of pairs of objects having different classes and different clusters, f11 is the number of pairs of objects having the same class and cluster, and f10 is the number of pair”
Finding Statistically Significant Communities in Networks (2010, neutral): “We measure two things, as a function of the number of noisy vertices: the similarity between the set of noisy vertices and the set of homeless vertices found by OSLOM, which is expressed by the Jaccard Index [59] (Fig.”
Data clustering: 50 years beyond K-means (2008, neutral): “Clustering algorithms have also been extensively studied in data mining (see books by Han and Kamber (2000) and Tan et al. (2005) and machine learning (Bishop, 2006).”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, neutral): “Abundant literature can be found presenting a tremendous progress in the topic [51, 98].”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, neutral): “Both association discovery and classification rules mining are essential in practical data mining applications [11], [22], and their integration could result in greater savings and convenience for the user.”
Community Detection and Mining in Social Media (2010, neutral): “So conventional classification measures (Tan et al., 2005) such as accuracy, F1-measure can be also used.”
Isolation-Based Anomaly Detection (2012, positive): “Two common ways to define distancebased anomaly score are (i) the distance to k nearest neighbour and (ii) the average distance to k-nearest neighbours [Tan et al. 2005].”