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A Framework for Clustering Evolving Data Streams
TLDR
This paper discusses a fundamentally different philosophy for data stream clustering which is guided by application-centered requirements. Expand
  • 1,735
  • 229
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Neural Networks and Deep Learning
  • C. Aggarwal
  • Computer Science
  • Springer International Publishing
  • 26 August 2018
  • 945
  • 142
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Fast algorithms for projected clustering
TLDR
The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Expand
  • 1,098
  • 120
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On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
TLDR
In this paper, we view the dimensionality curse from the point of view of the distance metrics which are used to measure the similarity between objects. Expand
  • 1,374
  • 84
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On the design and quantification of privacy preserving data mining algorithms
TLDR
We show that when a large amount of data is available, the EM algorithm provides robust estimates of the original distribution. Expand
  • 1,100
  • 78
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Outlier Analysis
  • C. Aggarwal
  • Computer Science
  • Springer New York
  • 11 January 2013
TLDR
Outlier Analysis is a comprehensive exposition of methods and techniques commonly used in outlier analysis, as understood by data mining experts, statisticians and computer scientists. Expand
  • 811
  • 73
Graph Clustering
  • C. Aggarwal
  • Computer Science
  • Encyclopedia of Machine Learning
  • 2010
  • 1,108
  • 71
Finding generalized projected clusters in high dimensional spaces
TLDR
We discuss very general techniques for projected clustering which are able to construct clusters in arbitrarily aligned subspaces of lower dimensionality. Expand
  • 514
  • 63
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Data Mining: The Textbook
TLDR
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. Expand
  • 502
  • 56
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Outlier detection for high dimensional data
TLDR
The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Expand
  • 879
  • 52
  • PDF