Name
Abstract | ||
|---|---|---|
Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied extensively in various research fields, including data mining, machine learning, pattern recognition, and in scientific, engineering, social, economic, and biomedical data analysis. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimates clustering algorithm for diagnosing medical diseases. The proposed hybrid system is first tested on well-known Iris data set and then on three data sets for diagnosing medical diseases from UCI data repository. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-92639-1_62 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018) |
Keywords | Field | DocType |
Data clustering, Maximum likelihood estimates clustering, Number of clusters, Fuzzy partition method | Data set,Fuzzy partition,Computer science,Maximum likelihood,Information repository,Artificial intelligence,Iris flower data set,Cluster analysis,Hybrid system,Machine learning | Conference |
Volume | ISSN | Citations |
10870 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Svetlana Simic | 1 | 40 | 12.78 |
| Zorana Banković | 2 | 112 | 16.91 |
| Dragan Simic | 3 | 40 | 12.78 |
| Svetislav Simic | 4 | 2 | 4.13 |