@article{2262891238ff45ae866645ebc2b3a252,
title = "Clicks: An effective algorithm for mining subspace clusters in categorical datasets",
abstract = "We present a novel algorithm called Clicks, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, Clicks mines subspace clusters. It uses a selective vertical method to guarantee complete search. Clicks outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. These results are demonstrated in a comprehensive performance study on real and synthetic datasets.",
keywords = "Categorical data, Clustering, k-Partite graph, Maximal cliques",
author = "Zaki, \{Mohammed J.\} and Markus Peters and Ira Assent and Thomas Seidl",
note = "Funding Information: Mohammed J. Zaki is an Associate Professor of Computer Science at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining techniques for bioinformatics, and other applications. He has published over 100 papers on data mining and co-edited 11 books. He is currently an associate editor for IEEE Transactions on Knowledge and Data Engineering, action editor for Data Mining and Knowledge Discovery, and on the editorial board of Int{\textquoteright}l Journal of Data Warehousing and Mining, Int{\textquoteright}l Journal of Data Mining and Bioinformatics, Scientific Programming and the ACM SIGMOD Digital Symposium Collection. He received the NSF Career Award in 2001 and the DOE Career Award in 2002. He also received a recognition of service award from ACM in 2003, and a certificate of appreciation from IEEE in 2005. ",
year = "2007",
month = jan,
doi = "10.1016/j.datak.2006.01.005",
language = "English",
volume = "60",
pages = "51--70",
journal = "Data and Knowledge Engineering",
issn = "0169-023X",
publisher = "Elsevier B.V.",
number = "1",
}