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KDD
2000
ACM
77views Data Mining» more  KDD 2000»
13 years 11 months ago
Small is beautiful: discovering the minimal set of unexpected patterns
A drawback of most traditional data mining methods is that they do not leverage prior knowledge of users. In many business settings, managers and analysts have significant intuiti...
Balaji Padmanabhan, Alexander Tuzhilin
ICDM
2003
IEEE
125views Data Mining» more  ICDM 2003»
14 years 1 months ago
Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns
The data stream captured by recording inhabitantdevice interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automa...
Edwin O. Heierman III, Diane J. Cook
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 8 months ago
Assessing data mining results via swap randomization
The problem of assessing the significance of data mining results on high-dimensional 0?1 data sets has been studied extensively in the literature. For problems such as mining freq...
Aristides Gionis, Heikki Mannila, Panayiotis Tsapa...
ACSC
2002
IEEE
14 years 22 days ago
Using Finite State Automata for Sequence Mining
We show how frequently occurring sequential patterns may be found from large datasets by first inducing a finite state automaton model describing the data, and then querying the m...
Philip Hingston
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 8 months ago
V-Miner: using enhanced parallel coordinates to mine product design and test data
Analyzing data to find trends, correlations, and stable patterns is an important task in many industrial applications. This paper proposes a new technique based on parallel coordi...
Kaidi Zhao, Bing Liu, Thomas M. Tirpak, Andreas Sc...