An Efficient Frequent Temporal Pattern Mining Algorithm
B. Sivaselvan .,N.P. Gopalan . +1 more
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About: This article is published in Information Technology Journal. The article was published on 01 Jun 2006. and is currently open access.
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Citations
Patent
Device for determining potential future interests to be introduced into profile(s) of user(s) of communication equipment(s)
Jerome Picault,Dimitre Kostadinov,Makram Bouzid +2 more
- 24 Sep 2010
TL;DR: In this paper, the authors propose a system for determining potential interests of users (U1-U3) that are clients of at least one network operator, each user being associated to a profile defining at least his interests.
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Efficient Algorithms for Video Association Mining
B. Sivaselvan,N.P. Gopalan +1 more
- 28 May 2007
TL;DR: The existing Apriori based algorithm is compared with three other approaches highlighting the case specific situations suited by each and the issue of frequent temporal pattern mining is addressed.
8
Mining Video Association Rules Based on Weighted Temporal Concepts
V. Vijayakumar,R. Nedunchezhian +1 more
- 01 Jan 2012
TL;DR: The proposed Modified HITS based weighted temporal concept did not require pre-assigned weights and has more practical significance than the traditional classical association rule mining algorithms.
A novel method for mining video association rules using weighted temporal tree
V. Vijayakumar,R. Nedunchezhian +1 more
- 01 Jan 2012
TL;DR: An efficient method for discovering a weighted temporal association rules from a large volumes of video sequence data in a single scan of the database using Weighted Temporal Tree structure is discussed.
Object oriented approach to prefix based fast mining of closed sequential patterns
L. P. Kumar,S. P. Kumar,D. R. Giri,V. Jayavani +3 more
- 26 Jul 2012
TL;DR: A novel algorithm to mine closed sequential patterns using an inverted matrix and prefix based sequence element matrix to minimize the scans required at levels k and k+1 in the mining process is proposed.
References
SPADE: An Efficient Algorithm for Mining Frequent Sequences
TL;DR: SPADE is a new algorithm for fast discovery of Sequential Patterns that utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations.
Mining sequential patterns by pattern-growth: the PrefixSpan approach
Jian Pei,Jiawei Han,Behzad Mortazavi-Asl,Jianyong Wang,Helen Pinto,Qiming Chen,U. Dayal,Meichun Hsu +7 more
TL;DR: This paper proposes a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns, and shows that PrefixSpan outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE and is the fastest among all the tested algorithms.
Video data mining: semantic indexing and event detection from the association perspective
TL;DR: This paper presents a knowledge-based video indexing and content management framework for domain specific videos, and provides a solution to explore video knowledge by mining associations from video data.
•Journal Article
Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective - Appendices.
TL;DR: In this paper, a knowledge-based video indexing and content management framework for domain specific videos is presented, which uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause) and introduces multilevel sequential association mining to explore associations among the audio and visual cues.
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