Results 201 to 210 of about 616 (224)

Mining and visual exploration of closed contiguous sequential patterns in trajectories

International Journal of Geographical Information Science, 2017
ABSTRACTLarge collections of trajectories provide rich insight into movement patterns of the tracked objects. By map matching trajectories to a road network as sequences of road edge IDs, contiguous sequential patterns can be extracted as a certain number of objects traversing a specific path, which provides valuable information in travel demand ...
Can Yang 0001, Gyözö Gidófalvi
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Efficient Mining of Closed Sequential Patterns on Stream Sliding Window

2011 IEEE 11th International Conference on Data Mining, 2011
As a typical data mining research topic, sequential pattern mining has been studied extensively for the past decade. Recently, mining various sequential patterns incrementally over stream data has raised great interest. Due to the challenges of mining stream data, many difficulties not so obvious in static data mining have to be reconsidered carefully.
Chuancong Gao, Jianyong Wang 0001, Qingyan Yang   +2 more
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A New Algorithm for Mining Weighted Closed Sequential Pattern

2009 Second International Symposium on Knowledge Acquisition and Modeling, 2009
Most previous sequential mining algorithms have the following two main drawbacks: On one hand, all sequential patterns are treated uniformly while sequential patterns have different importance. On the other hand, most of the sequence mining algorithms still generate an exponentially large number of sequential patterns when a minimum support is lowered.
Jinhong Li, Bingru Yang, Wei Song
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Developing a Web Recommendation System Based on Closed Sequential Patterns

2010
The proposed system is mainly based on mining closed sequential web access patterns. Initially, the PrefixSpan algorithm is employed on the preprocessed web server log data for mining sequential web access patterns. Subsequently, with the aid of post-pruning strategy, the closed sequential web access patterns are discovered from the complete set of ...
Utpala Niranjan, R. B. V. Subramanyam, V. Khanaa   +2 more
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Fast discovery of frequent closed sequential patterns based on positional data

2010 International Conference on Machine Learning and Cybernetics, 2010
Frequent closed sequential patterns mining is one of the hot topics in data mining. In this paper, a novel frequent closed sequential pattern mining algorithm, FCSM-PD (frequent closed sequential pattern mining algorithm based on positional data) is proposed, which is the improved BIDE algorithm based on the positional data. The positional data is used
Guo-Yan Huang, Fei Yang, Chang-Zhen Hu, Jia-Dong Ren   +3 more
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MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

2013
The problem of mining sequential patterns has been widely studied and many efficient algorithms used to solve this problem have been published. In some cases, there can be implicitly or explicitely defined taxonomies (hierarchies) over input items (e.g. product categories in a e-shop or sub-domains in the DNS system).
Michal Sebek, Martin Hlosta, Jaroslav Zendulka, Tomas Hruska   +3 more
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An Obstruction-Check Approach to Mining Closed Sequential Patterns in Data Streams

2015
Online mining sequential patterns over data streams is an important problem in data mining. There are many applications of using sequential patterns in data streams, such as market analysis, network security and web tracking. For solving the problem of data mining in the time-based sliding window, Chang et al.
Ye-In Chang, Chia-En Li, Tzu-Lin Chin
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On top-k closed sequential patterns mining

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2014
Jing Wang, Lei Zhang 0060, Guiquan Liu, Qi Liu 0003, Enhong Chen   +4 more
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Accelerated Frequent Closed Sequential Pattern Mining for uncertain data

Expert Systems with Applications, 2022
Tao You, Yue Sun, Ying Zhang 0060, Jinchao Chen, Peng Zhang, Mei Yang   +5 more
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CloSpan: Mining: Closed Sequential Patterns in Large Datasets

Proceedings of the 2003 SIAM International Conference on Data Mining, 2003
Xifeng Yan, Jiawei Han 0001, Ramin Afshar   +2 more
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