Comparison of the Accuracy of the Probabilistic Distance Clustering Method and Cluster Ensembles
DOI:
https://doi.org/10.18778/0208-6018.322.07Keywords:
clustering, accuracy, distance clustering method, cluster ensembleAbstract
High accuracy of results is a very important aspect in any clustering problem t determines the effectiveness of decisions based on them. Therefore, literature proposes methods and solutions that aim to give more accurate and stable results than traditional clustering algorithms (e.g. k-means or hierarchical methods). Cluster ensembles (Leisch 1999; Dudoit, Fridlyand 2003; Hornik 2006; Fred, Jain 2002) or the distance clustering method (Ben-Israel, Iyigun 2008) are the examples of such solutions. Here, we carry out an experimental study to compare the accuracy of these two approaches.
Downloads
References
Ben-Israel A., Iyigun C. (2008), Probabilistic d-clustering, “Journal of Classification”, 25(1), pp. 5–26.
Dudoit S., Fridlyand J. (2003), Bagging to improve the accuracy of a clustering procedure, “Bioinformatics”, vol. 19, no. 9, pp. 1090–1099.
Fred A., Jain A. K. (2002), Data clustering using evidence accumulation, “Proceedings of the Sixteenth International Conference on Pattern Recognition”, pp. 276–280.
Hornik K. (2005), A CLUE for CLUster ensembles, “Journal of Statistical Software”, 14, pp. 65–72.
Leisch F. (1999), Bagged clustering, “Adaptive Information Systems and Modeling in Economics and Management Science”, Working Papers, SFB, 51.





