Name
Abstract | ||
|---|---|---|
Data stream mining is an active area of research that poses challenging research problems. In the latter years, a variety of data stream clustering algorithms have been proposed to perform unsupervised learning using a two-step framework. Additionally, dealing with non-stationary, unbounded data streams requires the development of algorithms capable of performing fast and incremental clustering addressing time and memory limitations without jeopardizing clustering quality. In this paper we present CNDenStream, a one-step data stream clustering algorithm capable of finding non-hyper-spherical clusters which, in opposition to other data stream clustering algorithms, is able to maintain updated clusters after the arrival of each instance by using a complex network construction and evolution model based on homophily. Empirical studies show that CNDenStream is able to surpass other algorithms in clustering quality and requires a feasible amount of resources when compared to other algorithms presented in the literature. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26532-2_68 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data mining,Data stream mining,Homophily,Computer science,Unsupervised learning,Complex network,Artificial intelligence,Cluster analysis,Empirical research,Canopy clustering algorithm,Data stream clustering,Pattern recognition,Algorithm,Machine learning | Conference | 9489 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean Paul Barddal | 1 | 140 | 16.77 |
| Heitor Murilo Gomes | 2 | 155 | 17.36 |
| Fabrício Enembreck | 3 | 274 | 38.42 |