A methodology for dynamic data mining based on fuzzy clustering

Crespo, Fernando; Weber, Richard W.

doi:10.1016/j.fss.2004.03.028

Cited by 122 publications

(54 citation statements)

References 11 publications

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“…Among the most important ones are decision trees, neural networks, association rules and clustering methods (Crespoa and Weber, 2005;Loganantharaj et al, 2000).…”

Section: Recent Developments Of Dynamic Data Miningmentioning

confidence: 99%

“…During the first phase, Chameleon uses a graph-partitioning algorithm to cluster the data items into several relatively small subs to find the genuine clusters by repeatedly combining these sub-clusters. During the second phase, it uses an agglomerative hierarchical clustering algorithm to find the genuine clusters by repeatedly combining together these sub-clusters (Crespoa and Weber, 2005;Goura et al, 2011;Goyal et al, 2011).…”

Section: Davies-bouldin Index []mentioning

confidence: 99%

“…It enforced that object to be clustered during the process based on its property. Dynamic clustering is a mechanism to adopt the clustering in real time environments such as mobile computing, war-end movement observation (Crespoa and Weber, 2005). Dynamic data mining is increasingly attracting attention from the respective research community.…”

Section: Introductionmentioning

confidence: 99%

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A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset

Mary¹

2012

Journal of Computer Science

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Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a density based dynamic data clustering algorithm for clustering incremental dataset and compare its performance with full run of normal DBSCAN, Chameleon on the dynamic dataset. Most of the clustering algorithms perform well and will give ideal performance with good accuracy measured with clustering accuracy, which is calculated using the original class labels and the calculated class labels. However, if we measure the performance with a cluster validation metric, then it will give another kind of result. Results: This study addresses the problems of clustering a dynamic dataset in which the data set is increasing in size over time by adding more and more data. So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI), Davies-Bouldin index (DB) as the cluster validation metric and as well as time taken for clustering. Conclusion: In this study, we have successfully implemented and evaluated the proposed density based dynamic clustering algorithm. The performance of the algorithm was compared with Chameleon and DBSCAN clustering algorithms. The proposed algorithm performed significantly well in terms of clustering accuracy as well as speed.

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“…Among the most important ones are decision trees, neural networks, association rules and clustering methods (Crespoa and Weber, 2005;Loganantharaj et al, 2000).…”

Section: Recent Developments Of Dynamic Data Miningmentioning

confidence: 99%

Section: Davies-bouldin Index []mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset

Mary¹

2012

Journal of Computer Science

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“…It has been shown that the generated sampling method can be very effective for all defect detection despite small sampling size. Crespo and Weber [82] presented a fuzzy c means clustering based methodology for dynamic data mining and applied it on real life data. In the era of web based design and manufacturing, web mining is now frequently used to mine useful information.…”

Section: Clustering In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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“…The general view on advantages and delimitations on various bio-inspired methods combinations is proposed in the form of a decision tree. There are so many combinations of bio-inspired methods (Crespo & Weber, 2005;Georgieva & Klawonn, 2008;Jaimes & Torra, 2010;Kaiser & et. al., 2003Kaiser & et.…”

Section: Introductionmentioning

confidence: 99%