A Novel Density-Based Clustering Framework by Using Level Set Method

In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundar...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on knowledge and data engineering Ročník 21; číslo 11; s. 1515 - 1531
Hlavní autori: WANG, Xiao-Feng, HUANG, De-Shuang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.11.2009
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Applied sciences
Artificial intelligence
Boundaries
Clustering
Clustering algorithms
Clustering methods
Clusters
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Costs
Data mining
Data processing. List processing. Character string processing
Density
Density-based clustering
Evolution
Exact sciences and technology
Image retrieval
Image segmentation
Information retrieval
initial boundary
Labeling
Level set
level set density
level set method
LSD
Memory organisation. Data processing
Pattern classification
Pattern recognition. Digital image processing. Computational geometry
Software
Studies
valley seeking clustering
Valleys
Cluster analysis
Termination problem
Contour line
Data center
initial boundary
Outlier
Image segmentation
level set density
valley seeking clustering
Classification
level set method
Computer center
Density-based clustering
Point group
ISSN:1041-4347, 1558-2191
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundary formation scheme. Accordingly, three types of initial boundaries are defined so that each of them can evolve to approach the cluster centers in different ways. To avoid the long iteration time of level set evolution in data space, an efficient termination criterion is presented to stop the evolution process in the circumstance that no more cluster centers can be found. Then, a new effective density representation called level set density (LSD) is constructed from the evolution results. Finally, the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. The experiments on some synthetic and real data sets have demonstrated the efficiency and effectiveness of the proposed clustering framework. The comparisons with DBSCAN method, OPTICS method, and valley seeking clustering method further show that the proposed framework can successfully avoid the overfitting phenomenon and solve the confusion problem of cluster boundary points and outliers.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2009.21