The Handbook of Data Mining

By Nong Ye. 2003

351 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Rank-Based Similarity Search: Reducing the Dimensional Dependence (2015, methodology): “For data mining applications, the approaches considered have included feature sampling for local outlier detection [15], data sampling for clustering [50], and approximate similarity search for k -NN classiﬁcation (as well as in its own right).”
Emerging multinationals from mid-range economies: the influence of institutions and factor markets (2013, methodology): “To increase performance, there is an incremental version of K-means that allows the centres of the clusters to be recalculated as each observation is added to the clusters, rather than waiting until each iteration before recalculating the mean (Ghosh, 2003).”
Intersectionality and Disability Harassment (2012, methodology): “Data mining has been used extensively in business for evaluating credit, predicting wages, and segmenting the marketplace (Nong, 2003; SPSS, 1998).”
An Optimal Constrained Pruning Strategy for Decision Trees (2009, methodology): “Other examples of objective criteria that are used in data mining contexts, which we can accommodate as well, are the error rate and the standard error (see Ye 2003).”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “(n) test instance is predicted as normal (N) it is known as true negative (TN), while it is a false positive (FP) if it is predicted as anomalous (Y) [40], [227], [228].”