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HICSS
2010
IEEE
258views Biometrics» more  HICSS 2010»
15 years 6 months ago
An Empirical Comparison of Four Text Mining Methods
The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various...
Sangno Lee, Jeff Baker, Jaeki Song, James C. Wethe...
ICDM
2003
IEEE
140views Data Mining» more  ICDM 2003»
15 years 11 months ago
Mining Frequent Itemsets in Distributed and Dynamic Databases
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed...
Matthew Eric Otey, Chao Wang, Srinivasan Parthasar...
CAEPIA
2003
Springer
15 years 11 months ago
Clustering Main Concepts from e-Mails
E–mail is one of the most common ways to communicate, assuming, in some cases, up to 75% of a company’s communication, in which every employee spends about 90 minutes a day in ...
Jesús S. Aguilar-Ruiz, Domingo S. Rodr&iacu...
KDD
2001
ACM
169views Data Mining» more  KDD 2001»
16 years 6 months ago
Hierarchical cluster analysis of SAGE data for cancer profiling
In this paper we present a method for clustering SAGE (Serial Analysis of Gene Expression) data to detect similarities and dissimilarities between different types of cancer on the...
Jörg Sander, Monica C. Sleumer, Raymond T. Ng
PAKDD
2004
ACM
131views Data Mining» more  PAKDD 2004»
15 years 11 months ago
Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models
This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the ...
Qiming Huang, Qiang Yang, Joshua Zhexue Huang, Mic...