Name
Abstract | ||
|---|---|---|
Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new criterion of direction interestingness, which incorporates information from the density of the projected data. Subsequently, we utilize the Differential Evolution algorithm to perform optimization over the space of the projections and hence construct a new hierarchical clustering algorithmic scheme. The new algorithm shows promising performance over a series of real and simulated data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CEC.2012.6253006 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
data mining,evolutionary computation,pattern clustering,principal component analysis,data mining,density based projection pursuit clustering,differential evolution algorithm,direction interestingness,hierarchical clustering algorithmic scheme,high dimensional data clustering,principal component analysis,projected data density,real data,simulated data | Data mining,CURE data clustering algorithm,Projection pursuit,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Determining the number of clusters in a data set,Constrained clustering,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-1508-1 | 1 | 0.36 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sotiris K. Tasoulis | 1 | 2 | 0.73 |
| Michael G. Epitropakis | 2 | 81 | 3.67 |
| Plagianakos, V.P. | 3 | 173 | 13.01 |
| Dimitris K. Tasoulis | 4 | 89 | 6.43 |