SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows

Chang, Lei; Wang, Tengjiao; Yang, Dongqing; Luan, Hua

doi:10.1109/icdm.2008.36

Cited by 31 publications

(22 citation statements)

References 18 publications

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“…To mine frequent closed sequence patterns on stream data, CISpan [21] was proposed based on CloSpan's approach, which tries to mine a CloSpan-style enumeration tree on both the new data and the previously affected data, and then merge it with the previous tree together to build a new tree for the updated data. Another algorithm SeqStream [4] was also proposed, which solves a different closed sequential patterns mining problem over stream data where new items are appended to the ends of the existing sequences incrementally.…”

Section: Related Workmentioning

confidence: 99%

Efficient Mining of Closed Sequential Patterns on Stream Sliding Window

Gao

Wang

Yang

2011

2011 IEEE 11th International Conference on Data Mining

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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. In this paper, we propose a novel algorithm which stores only frequent closed prefixes in its enumeration tree structure, used for mining and maintaining patterns in the current sliding window, to solve the frequent closed sequential pattern mining problem efficiently over stream data. Some effective search space pruning and pattern closure checking strategies have been also devised to accelerate the algorithm. Experimental results show that our algorithm outperforms other state-of-the-art algorithm significantly in both running time and memory use.

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Section: Related Workmentioning

confidence: 99%

Efficient Mining of Closed Sequential Patterns on Stream Sliding Window

Gao

Wang

Yang

2011

2011 IEEE 11th International Conference on Data Mining

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“…Since then, sequential pattern mining algorithms have been extensively developed such as constraint-based sequential pattern mining [13,17], closed sequential pattern mining [20], approximate sequence mining [10], multi-dimensional sequence pattern mining [16], sequence mining in a noisy environment [21], biological sequence mining [7,19], incremental sequence mining [5] and sequence indexing [6], sequential affinity pattern mining [22], closed sequential pattern mining [20] and stream pattern mining [4]. Moreover, to focus on the user's interestingness, constraint based sequential pattern mining [13,17] has been suggested.…”

Section: Related Workmentioning

confidence: 99%

Weighted approximate sequential pattern mining within tolerance factors

Yun

Ryu

Yoon

2011

IDA

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In data mining area, weight based sequential pattern mining has been suggested to find important sequential patterns by considering the weights of sequential patterns. More extensions with weight constraints have been proposed such as mining weighted association rules, weighted sequential patterns, weighted closed patterns, frequent patterns with dynamic weights, weighted graphs, weighted sub-trees or sub structures, and so on. In previous approach of weighted frequent sequential pattern mining, weighted supports of sequential patterns are exactly matched to prune weighted infrequent sequential patterns. However, in the noisy environment, the small change in weights or supports of items affects the result sets seriously. This may make the weighted sequential patterns less useful in the noisy environment. In this paper, we propose the robust concept of mining weighted approximate sequential patterns. Based on the framework of weight based sequential pattern mining, an approximate factor is defined to relax the requirement for exact equality between weighted supports of sequential patterns and a minimum threshold. After then, we address the concept of mining weighted approximate sequential frequent patterns to find important sequential patterns with/without the noisy data. We analyze the characteristics of weighted approximate sequential patterns and run extensive performance tests.

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“…A customer's moving profile is formed using the set of closed sequential patterns that match the customer's trajectory and the profile is incrementally maintained. We developed a novel algorithm [1] to mine and incrementally maintain on fast data streams closed sequential patterns, which are non-redundant representation of sequential patterns. An effective data structure is designed to keep close sequential patterns in memory and various strategies are proposed to prune search space aggressively.…”

Section: Technology and Noveltymentioning

confidence: 99%

“…Based on the experiments on both real and synthetic databases, our algorithm outperforms the best existing al- gorithms by a large margin. The details of the techniques can be found in [1].…”

Section: Technology and Noveltymentioning

confidence: 99%

MobileMiner

Wang

Yang

Gao

et al. 2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

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Mobile communication data analysis has been often used as a background application to motivate many data mining problems. However, very few data mining researchers have a chance to see a working data mining system on real mobile communication data. In this demo, we showcase our new system MobileMiner on a real mobile communication data set, which presents a case study of business solutions using state-of-the-art data mining techniques. MobileMiner adaptively profiles users' behavior from their calling and moving record streams. Customer segmentation and social community analysis can be conducted based on user profiles. We show how data mining techniques can help in mobile communication data analysis. Moreover, we also show some interesting observations which still cannot be mined by the current techniques, and thus may motivate new research and development.

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SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows

Cited by 31 publications

References 18 publications

Efficient Mining of Closed Sequential Patterns on Stream Sliding Window

Efficient Mining of Closed Sequential Patterns on Stream Sliding Window

Weighted approximate sequential pattern mining within tolerance factors

MobileMiner

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