Name
Abstract | ||
|---|---|---|
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks. |
Year | DOI | Venue |
|---|---|---|
2009 | null | IJCAI |
Keywords | Field | DocType |
historical model distribution,historical data distribution,historical data,conventional clustering,current model distribution,evolutionary clustering,historical model,on-line evolutionary exponential family,current data distribution,clustering problem,clustering data,density estimation,exponential family | Density estimation,Data mining,Computer science,Exponential family,Data dependent,Evolutionary clustering,Cluster analysis | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
6 | 0.65 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianwen Zhang | 1 | 319 | 14.74 |
| Yangqiu Song | 2 | 2045 | 103.29 |
| Gang Chen | 3 | 793 | 75.07 |
| Changshui Zhang | 4 | 5506 | 323.40 |