Open Access
Data Mining - Concepts and Techniques.
Petra Perner
- Vol. 16, pp 77
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The article was published on 2002-01-01 and is currently open access. It has received 9314 citations till now. The article focuses on the topics: Web mining & Concept mining.read more
Citations
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Book
Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
Book ChapterDOI
Data Clustering: 50 Years Beyond K-means
TL;DR: Cluster analysis as mentioned in this paper is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics, which is one of the most fundamental modes of understanding and learning.
Journal ArticleDOI
A survey of collaborative filtering techniques
TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Book ChapterDOI
A Survey of Clustering Data Mining Techniques
TL;DR: This survey concentrates on clustering algorithms from a data mining perspective as a data modeling technique that provides for concise summaries of the data.
Journal ArticleDOI
On Clustering Validation Techniques
TL;DR: The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated.
References
More filters
Book
Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
Journal ArticleDOI
A survey of collaborative filtering techniques
TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Book ChapterDOI
A Survey of Clustering Data Mining Techniques
TL;DR: This survey concentrates on clustering algorithms from a data mining perspective as a data modeling technique that provides for concise summaries of the data.
Journal ArticleDOI
On Clustering Validation Techniques
TL;DR: The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated.
Journal ArticleDOI
Toward integrating feature selection algorithms for classification and clustering
TL;DR: With the categorizing framework, the efforts toward-building an integrated system for intelligent feature selection are continued, and an illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms.