A New Incremental Updating Algorithm for Mining Sequential Patterns

Ren, Jiadong; Zhou, Xiaolei

doi:10.3844/jcssp.2006.318.321

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Sequential pattern mining, which finds out frequent patterns in a sequence database, is a significant issue among the various data mining issues [18]. Discovering sequential patterns from a huge database of sequences is a significant issue in the field of knowledge discovery and data mining [19].…”

Section: Sequential Pattern Miningmentioning

confidence: 99%

A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

Kochar

Chhillar

2011

Int J Wireless Inf Networks

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Data warehousing and Data mining find enormous applications; RFID technology is one among them. A RFID data warehousing system with novel data cleaning, transformation and loading technique has been proposed in the previous work. The system has been dedicatedly implemented in one of the significant RFID applications tracking of goods in warehouses. The warehoused RFID data is in specific format and so an effective mining system is required to mine the needed information from the database. The existing mining algorithms are inefficient in extracting the information from the warehoused RFID data. In this paper, a novel data mining system is proposed, which effectively extracts the information regarding the nature of movement of the RFID tags. The proposed mining system generates an intermediate dataset (I-dataset) from the warehoused dataset. From the I-dataset, sequential patterns are mined with different pattern length combinations. From the mined sequential patterns, fuzzy rules are generated, which depicts the nature of movement of the RFID tags. The implementation results show that the proposed mining system performs well by extracting the significant RFID tags and its combinations and the nature of movement of the tags.

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Section: Sequential Pattern Miningmentioning

confidence: 99%

A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

Kochar

Chhillar

2011

Int J Wireless Inf Networks

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“…Combined mining is best used to provide actionable knowledge in spite of complex data sets and features. Ren and Zhou (2006) Sequential patterns obtained from the prior mining processes.…”

Section: Related Workmentioning

confidence: 99%

Combined Pattern Mining for D3m Using Fuzzy

Dhenakaran

Maheswari

2013

American Journal of Applied Sciences

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Many algorithms and models were developed but the findings are not actionable and lack of soft power while solving the complex problems. Domain Driven Data mining is used a major efforts to promote the action ability of the knowledge discovery in the real world smart decision making. Combined mining is one of the common methods for analyzing complex data for identifying complex knowledge. The deliverables of combined mining are combined patterns. The complex environment gives the combined patterns. In this research we process a new technique called Fuzzy Combined Pattern Mining (FCPM) for Domain Driven Data Mining. It was used to find all the rules that satisfy the minimum support and minimum confidence constraints. In FCPM, we first apply the fuzzy concept to find the patterns after that the fuzzy pattern will be merged to combined pattern mining. The proposed algorithm have been implemented and compared with Apriori Its performance was studied on an experimental basis. The main objective is to provide the interesting patterns to the end user. The implementation of fuzzy in combined mining will generate the rules and based on rules we can identify the interesting patterns.

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“…Vijayalakshmi and Mohan, (2010) discussed how to maintain discovered sequential patterns when some information is deleted from a sequence database. Ren and Zhou, (2006) approach resulted in the generation of usage profiles and automatic identification of user interest in each profile. Sumathi et al (2010) developed an application of session based clustering to analyze web pages of user interest from web log files.…”

Section: Previous Work On Wummentioning

confidence: 99%

IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

Almurtadha¹,

Sulaiman²,

Mustapha³

et al. 2011

American Journal of Applied Sciences

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Problem statement: Recently, Web usage mining techniques have been widely used to build recommendation systems especially for anonymous users. Approach: Assigning the current user to the best web navigation profile with similar navigation activities will improve the ability of the prediction engine to produce a recommendation list then introduce it to the user. This study presents iPACT an improved recommendation system using Profile Aggregation based on Clustering of Transactions (PACT). Results: iPACT shows better prediction accuracy than the previous methods PACT and Hypergraph. Conclusion: The users' interests change over time; hence an incremental and adaptive web navigation profiling is a key feature for the future works.

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A New Incremental Updating Algorithm for Mining Sequential Patterns

Cited by 6 publications

References 7 publications

A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

Combined Pattern Mining for D3m Using Fuzzy

IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions

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