@inbook{bfa506aaffe7438faee89cceb11c4488,
title = "Weighting Method for Feature Selection in K-Means",
abstract = "The k-means type of clustering algorithms [13, 16] are widely used in real world applications such as marketing research [12] and data mining due to their efficiency in processing large datasets. One unavoidable task of using k-means in real applications is to determine a set of features (or attributes). A common practice is to select features based on business domain knowledge and data exploration. This manual approach is difficult to use, time consuming, and frequently cannot make a right selection. An automated method is needed to solve the feature selection problem in k-means.",
author = "Huang, {Joshua Zhexue} and Jun Xu and Michael Ng and Yunming Ye",
year = "2007",
month = oct,
day = "29",
doi = "10.1201/9781584888796-19",
language = "English",
isbn = "9781584888789",
series = "Chapman & Hall/CRC Data Mining and Knowledge Discovery Series",
publisher = "CRC Press",
pages = "193--209",
editor = "Huan Liu and Hiroshi Motoda",
booktitle = "Computational Methods of Feature Selection",
edition = "1st",
}