Acknowledgements

four clusters as in Figure 11b–e, respectively. This algorithm can only be applied for grayscaled images. Cluster-1 shows the roads for the given landscape, cluster-2 shows the green areas in the landscape, cluster-3 shows the sea for the given landscape and cluster-4 represents

Figure 11. Genetic algorithm results. (a) Original image, (b) Cluster No:1, (c) Cluster No:2, (d) Cluster No:3, and (e)

In this part, we discussed the fundamental partitional clustering algorithms and its applications. Partitional clustering produces a partition using K clusters but do not use hierarchical structures. This type of clustering process uses criterion function in a range of error such as mean square error, sum of squared error, and so on. For this reason, partitional clustering is an

Some search techniques which include evolutionary process, provide seeking the global or local optima. Search techniques introduce more parameters than K-Means clustering process. There is no theoretical approval for selecting of the best approach. All the given methods can

The partitional clustering algorithms need high computational requirements. High computational requirements means that some complex algorithms need advanced operations. These requirements limit their applications in large data. Overcoming this situation, high speed and

high-capacity memory-sized computers can be used for clustering processes.

the air for the given landscape.

30 Recent Applications in Data Clustering

give the best results for different data.

7. Conclusion

Cluster No:4.

iterative process.

This study was performed at Gazi University, Engineering Faculty, Electrical & Electronics Engineering Department. MATLAB® 2016b software platform was used for this study. The study was performed in a computer with 12 GB RAM, Intel i5 processor at 2.6 Ghz.
