Name
Abstract | ||
|---|---|---|
Paper presents the work on development and validation of spatio-temporal clustering technique called ST-OPTICS.Emphasis has also been laid down on validation using performance indices.Capable to handle all types of dimensions i.e. spatial, non-spatial and temporal.It gives micro level clusters, agglomeration has been done for better visualization and interpretation.Results (average runtime & quality of clusters) found to be better than existing technique. Spatio-temporal data mining (STDM) is a process of the extraction of implicit knowledge, spatial and temporal relationships, or other patterns not explicitly stored in spatio-temporal databases. As data are growing not only from static view point, but they also evolve spatially and temporally which is dynamic in nature that is the reason why this field is now becoming very important field of research. In addition, spatio-temporal (ST) data tend to be highly auto-correlated, which leads to failure of assumption of independence, which is there in Gaussian distribution model. Vital issues in spatio temporal clustering technique for earth observation data is to obtain clusters of, good quality, arbitrary shape, problem of nested clustering and their validation. The present paper addresses these issues and proposes their solutions. In this direction, an attempt has been made to develop a clustering algorithm named as \"Spatio-Temporal - Ordering Points to Identify Clustering Structure (ST-OPTICS)\" which is modified version of existing density based technique - \"Ordering Points to Identify Clustering Structure (OPTICS)\". Experimental work carried out is analysed and found that quality of clusters obtained and run time efficiency are much better than existing technique i.e. ST-DBSCAN. In order to improve the visualization and the interpretation of obtained micro level clusters, sincere effort has been put in to merge the obtained clusters using agglomerative approach. Performance evaluation is done in both ways i.e. qualitatively and quantitatively for cross validating the results. Results show performance improvement of proposed ST-OPTICS clustering technique compared to ST-DBSCAN algorithm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2016.06.048 | Inf. Sci. |
Keywords | Field | DocType |
Spatio-temporal,Clustering,ST-OPTICS,ST-DBSCAN,Cluster validation indices | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Affinity propagation,Determining the number of clusters in a data set,Optics,Machine learning | Journal |
Volume | Issue | ISSN |
369 | C | 0020-0255 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. P. Agrawal | 1 | 0 | 0.68 |
| Sanjay Garg | 2 | 2 | 3.76 |
| Shashikant Sharma | 3 | 0 | 0.34 |
| Pinkal Patel | 4 | 0 | 0.68 |