Arbitrary Shape Cluster Algorithm for Clustering Data Stream

Now we are in the age of big data. Huge amount of data and information are generated every time. Traditional data stream algorithms are suit for the data streams with low dimension and simple structure. However, with the development of information technology, the produced data streams are becoming more and more complicated. It is particularly important to study how to find new associations and patterns from complex data to achieve the cognition ability and judgment ability like human brain. Clustering data streams with mixed attributes of irregular distribution is a big challenge in data mining. To solve this problem, we present an adaptive density data stream clustering algorithm-ADStream. ADStream is based on the online-off-line clustering framework. It can automatically recognize the initial clusters by passing messages between data points. Then a novel time-decay density clustering strategy is designed to group and update the continuously arriving data streams. Comprehensive experimental results demonstrate that ADStream is adaptive to the evolving data streams and may generate highquality clusters with fast processing rate.

show abstract

“…Remove density unit and reclaim memory; (17). end if (18). if ( t i mod Dt ð Þ¼ ¼0)//Check the attenuation of density unit (19).…”

Section: Convergence and Computational Complexity Analysis Of Adstreamentioning

confidence: 99%

An Adaptive Density Data Stream Clustering Algorithm

et al. 2015

View full text Add to dashboard Cite

show abstract

“…They suggested their algorithm based on the concept of static semi-supervised through and domain information in order to achieve highly satisfactory results. More studies related to clustering data streams are conducted in the literature such as AclueStream [153], OPClueStream [154], and ClusTree [155]. Also two extensive surveys on clustering data stream can be found in [156] and [157].…”

Section: Micro-clusteringmentioning

confidence: 99%

Data Summarization Techniques for Big Data—A Survey

Hesabi

Tari

Gościński

et al. 2015

Handbook on Data Centers

View full text Add to dashboard Cite

“…(c) to decide whether the selected data is middle data or not. Although p 4 , p 5 are not used by any other transitions except t 1 and t 2 , they are possibly the end outputs for user. Whether a data is middle data or not is decided by application, so an extra flag is required to indicate a middle data explicitly.…”

Section: Functions Mergingmentioning

confidence: 99%

“…The difference between data is called distance, the greater the distance is, the less similar to be. Many clustering algorithms had been presented; some of the examples are K-means Algorithm, CURE Algorithm, BIRCH Algorithm and so on [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

A Subsystem Division Method by Clustering

Wang¹

2008

2008 the 9th International Conference for Young Computer Scientists

View full text Add to dashboard Cite

State explosion problem is the primary obstacle to model complex system with Petri nets; modularization and hierarchy provide ways to solve this problem. When the bottom-up method is adopted, system functions in the lower layers are combined to obtain sub systems. The idea of clustering is introduced to decide which functions should be combined. The operation to combine two functions is defined; the distance between two functions is calculated by the degree of relevancy; a clustering algorithm with the idea of nearest-neighbor-first is designed to divide functions into sub systems. The function clustering method presented in this paper facilitates the Petri nets based system developing process.

show abstract

Arbitrary Shape Cluster Algorithm for Clustering Data Stream

Cited by 18 publications

References 9 publications

An Adaptive Density Data Stream Clustering Algorithm

An Adaptive Density Data Stream Clustering Algorithm

Data Summarization Techniques for Big Data—A Survey

A Subsystem Division Method by Clustering

Contact Info

Product

Resources

About