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» Mining evolving data streams for frequent patterns
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KDD
2006
ACM
147views Data Mining» more  KDD 2006»
15 years 10 months ago
Summarizing itemset patterns using probabilistic models
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interp...
Chao Wang, Srinivasan Parthasarathy
KDD
2000
ACM
121views Data Mining» more  KDD 2000»
15 years 1 months ago
Mining high-speed data streams
Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous dat...
Pedro Domingos, Geoff Hulten
KDD
2006
ACM
183views Data Mining» more  KDD 2006»
15 years 10 months ago
Discovering interesting patterns through user's interactive feedback
In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (i.e., frequent patterns) h...
Dong Xin, Xuehua Shen, Qiaozhu Mei, Jiawei Han
BDA
2000
14 years 11 months ago
Incremental Mining of Sequential Patterns in Large Databases
In this paper we consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database. We present a new a...
Florent Masseglia, Pascal Poncelet, Maguelonne Tei...
SDM
2012
SIAM
293views Data Mining» more  SDM 2012»
13 years 5 days ago
RP-growth: Top-k Mining of Relevant Patterns with Minimum Support Raising
One practical inconvenience in frequent pattern mining is that it often yields a flood of common or uninformative patterns, and thus we should carefully adjust the minimum suppor...
Yoshitaka Kameya, Taisuke Sato