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» Classes and clusters in data analysis
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CSB
2003
IEEE
150views Bioinformatics» more  CSB 2003»
15 years 3 months ago
Algorithms for Bounded-Error Correlation of High Dimensional Data in Microarray Experiments
The problem of clustering continuous valued data has been well studied in literature. Its application to microarray analysis relies on such algorithms as -means, dimensionality re...
Mehmet Koyutürk, Ananth Grama, Wojciech Szpan...
85
Voted
PERVASIVE
2009
Springer
15 years 5 months ago
Methodologies for Continuous Cellular Tower Data Analysis
This paper presents novel methodologies for the analysis of continuous cellular tower data from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of...
Nathan Eagle, John A. Quinn, Aaron Clauset
99
Voted
DEXA
2009
Springer
175views Database» more  DEXA 2009»
15 years 4 months ago
RoK: Roll-Up with the K-Means Clustering Method for Recommending OLAP Queries
Dimension hierarchies represent a substantial part of the data warehouse model. Indeed they allow decision makers to examine data at different levels of detail with On-Line Analyt...
Fadila Bentayeb, Cécile Favre
SDM
2009
SIAM
205views Data Mining» more  SDM 2009»
15 years 7 months ago
Identifying Information-Rich Subspace Trends in High-Dimensional Data.
Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in m...
Chandan K. Reddy, Snehal Pokharkar
SSPR
2004
Springer
15 years 3 months ago
Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data
Kernel Methods are a class of algorithms for pattern analysis with a number of convenient features. They can deal in a uniform way with a multitude of data types and can be used to...
Tijl De Bie, Nello Cristianini