Comparing Clustering Algorithms. Partitioning Algorithms K-Means DBSCAN Using KD Trees Hierarchical Algorithms Agglomerative Clustering CURE. K-Means Partitional clustering. Prototype based Clustering O(I * K * m * n) Space Complexity
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until the centroids do not change
- SPAETH2 2D dataset of 3360 points
Eg. Fast K-Means, Fast DBSCAN etc
We considered 2 popular Clustering Algorithms which use KD Tree Approach to speed up clustering and minimize search time.
We used Open Source Implementation of KD Trees (available under GNU GPL)
No. of Points 1572 3568 7502 10256
Clustering Time (sec) 3.5 10.9 39.5 78.4
Cure hence has a O(n) Space Complexity
Observations towards Sensitivity to Parameters
JDK 1.6, Eclipse, MATLAB, LABView, GnuPlot
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