Name
Abstract | ||
|---|---|---|
This paper discusses the problem of clustering data changing over time, a research domain that is attracting increasing attention due to the increased availability of streaming data in the Web 2.0 era. In the analysis conducted throughout the paper we make use of the kernel spectral clustering with memory (MKSC) algorithm, which is developed in a constrained optimization setting. Since the objective function of the MKSC model is designed to explicitly incorporate temporal smoothness, the algorithm belongs to the family of evolutionary clustering methods. Experiments over a number of real and synthetic datasets provide very interesting insights in the dynamics of the clusters evolution. Specifically, MKSC is able to handle objects leaving and entering over time, and recognize events like continuing, shrinking, growing, splitting, merging, dissolving and forming of clusters. Moreover, we discover how one of the regularization constants of the MKSC model, referred as the smoothness parameter, can be used as a change indicator measure. Finally, some possible visualizations of the cluster dynamics are proposed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIDM.2014.7008141 | Computational Intelligence and Data Mining |
Keywords | Field | DocType |
constraint handling,data analysis,evolutionary computation,pattern clustering,MKSC algorithm,Web 2.0 era,change indicator measure,cluster dynamics,constrained optimization setting,data clustering,data streaming,evolutionary clustering methods,kernel spectral clustering with memory algorithm,objective function,real datasets,regularization constants,smoothness parameter,synthetic datasets,temporal smoothness | Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rocco Langone | 1 | 164 | 13.41 |
| Raghvendra Mall | 2 | 170 | 17.53 |
| Johan A. K. Suykens | 3 | 635 | 53.51 |