Sciweavers

KDD
2004
ACM
211views Data Mining» more  KDD 2004»
14 years 12 months ago
Towards parameter-free data mining
Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings ...
Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) R...
KDD
2004
ACM
137views Data Mining» more  KDD 2004»
14 years 12 months ago
When do data mining results violate privacy?
Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where...
Murat Kantarcioglu, Jiashun Jin, Chris Clifton
KDD
2004
ACM
210views Data Mining» more  KDD 2004»
14 years 12 months ago
Web usage mining based on probabilistic latent semantic analysis
The primary goal of Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of user sessions and discoveri...
Xin Jin, Yanzan Zhou, Bamshad Mobasher
KDD
2004
ACM
145views Data Mining» more  KDD 2004»
14 years 12 months ago
Mining coherent gene clusters from gene-sample-time microarray data
Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of genesa...
Daxin Jiang, Jian Pei, Murali Ramanathan, Chun Tan...
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
14 years 12 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
KDD
2004
ACM
114views Data Mining» more  KDD 2004»
14 years 12 months ago
Mining the space of graph properties
Existing data mining algorithms on graphs look for nodes satisfying specific properties, such as specific notions of structural similarity or specific measures of link-based impor...
Glen Jeh, Jennifer Widom
KDD
2004
ACM
148views Data Mining» more  KDD 2004»
14 years 12 months ago
Interestingness of frequent itemsets using Bayesian networks as background knowledge
The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the abso...
Szymon Jaroszewicz, Dan A. Simovici
KDD
2004
ACM
124views Data Mining» more  KDD 2004»
14 years 12 months ago
Eigenspace-based anomaly detection in computer systems
We report on an automated runtime anomaly detection method at the application layer of multi-node computer systems. Although several network management systems are available in th...
Hisashi Kashima, Tsuyoshi Idé
KDD
2004
ACM
207views Data Mining» more  KDD 2004»
14 years 12 months ago
SPIN: mining maximal frequent subgraphs from graph databases
One fundamental challenge for mining recurring subgraphs from semi-structured data sets is the overwhelming abundance of such patterns. In large graph databases, the total number ...
Jun Huan, Wei Wang 0010, Jan Prins, Jiong Yang
KDD
2004
ACM
135views Data Mining» more  KDD 2004»
14 years 12 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker