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ML
2006
ACM
110views Machine Learning» more  ML 2006»
9 years 11 months ago
Distribution-based aggregation for relational learning with identifier attributes
Abstract Identifier attributes--very high-dimensional categorical attributes such as particular product ids or people's names--rarely are incorporated in statistical modeling....
Claudia Perlich, Foster J. Provost
KDD
1998
ACM
131views Data Mining» more  KDD 1998»
10 years 4 months ago
Interestingness-Based Interval Merger for Numeric Association Rules
We present an algorithm for mining association rules from relational tables containing numeric and categorical attributes. The approach is to merge adjacent intervals of numeric v...
Ke Wang, Soon Hock William Tay, Bing Liu
KDD
1999
ACM
166views Data Mining» more  KDD 1999»
10 years 4 months ago
CACTUS - Clustering Categorical Data Using Summaries
Clustering is an important data mining problem. Most of the earlier work on clustering focussed on numeric attributes which have a natural ordering on their attribute values. Rece...
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishn...
ICDM
2008
IEEE
118views Data Mining» more  ICDM 2008»
10 years 6 months ago
Extension of Partitional Clustering Methods for Handling Mixed Data
Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on the use of a distance measure ...
Yosr Naïja, Salem Chakhar, Kaouthar Blibech, ...
KDD
2008
ACM
110views Data Mining» more  KDD 2008»
11 years 7 days ago
Mining preferences from superior and inferior examples
Mining user preferences plays a critical role in many important applications such as customer relationship management (CRM), product and service recommendation, and marketing camp...
Bin Jiang, Jian Pei, Xuemin Lin, David W. Cheung, ...
ICDE
1999
IEEE
183views Database» more  ICDE 1999»
11 years 1 months ago
ROCK: A Robust Clustering Algorithm for Categorical Attributes
Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) simi...
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
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