FUAT – A Fuzzy Clustering Analysis Tool
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This document summarizes fuzzy c-means clustering (FCM) and introduces a software tool called FUAT that aims to address some of the difficulties with FCM. FCM is a soft clustering method that allows data elements to belong to more than one cluster. It is based on fuzzy set theory and combines c-means clustering with handling fuzziness in data. FUAT stands for Fuzzy Unsupervised Analysis Tool and provides features like automatic cluster number detection, interactive viewers for insights into results, and connectivity to R for further analysis. It aims to make fuzzy clustering more transparent and help with challenges like selecting initial centroids and evaluating clusters.
![Fuzzy c-means clustering Based on Zadeh’s fuzzy sets theory. Invented by Bezdek, 1981 A soft clustering method C ombines the c-means approach with the handling of the fuzziness existing in the data one of the most popular unsupervised c lustering algorithm, which is widely used in pattern recognition, image recognition, gene classification, etc [1]](https://image.slidesharecdn.com/fuatsunum-111129050437-phpapp01/85/FUAT-A-Fuzzy-Clustering-Analysis-Tool-5-320.jpg)
![Difficulties of Fuzzy c-means clustering as stated by [ 2 ], three major difficulties were drawn ; (1) how to detect optimal number of clusters ? (2) how to choos e the initial cluster centroids ? (3) how to evaluate cluster results, characterized by large variations in cluster shape, cluster density, and the number of points in different clusters](https://image.slidesharecdn.com/fuatsunum-111129050437-phpapp01/85/FUAT-A-Fuzzy-Clustering-Analysis-Tool-7-320.jpg)
![References [1] Jingwei Liu, Meizhi Xu, Kernelized fuzzy attribute C-means clustering algorithm, Fuzzy Sets and Systems 159 (2008) 2428 – 2445 [2] Dae-Won Kim , Kwang H. Lee, Doheon Lee, A novel initialization scheme for the fuzzy c-means algorithm for color clustering, Pattern Recognition Letters 25 (2004) 227–237](https://image.slidesharecdn.com/fuatsunum-111129050437-phpapp01/85/FUAT-A-Fuzzy-Clustering-Analysis-Tool-18-320.jpg)
FUAT – A Fuzzy Clustering Analysis Tool
- 1. A. Selman BOZKIR - Ebru Akçapınar Sezer Hacettepe University – Computer Eng. Dept
- 2. What is clustering and FCM? Principle of Fuzzy Clustering The difficulties in FCM Proposed solution: FUAT Details Conclusion Perspective
- 3. Clustering Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the distances of objects in the same cluster (intra class) are less than the distances in different cluster s (inter class) .
- 4. Clustering (Schemas) Hard Clustering (ex:k-means) Soft Clustering (ex: EM,FCM) each data element belongs to exactly one cluster elements can belong to more than one cluster, and associated with each element is a set of membership levels .
- 5. Fuzzy c-means clustering Based on Zadeh’s fuzzy sets theory. Invented by Bezdek, 1981 A soft clustering method C ombines the c-means approach with the handling of the fuzziness existing in the data one of the most popular unsupervised c lustering algorithm, which is widely used in pattern recognition, image recognition, gene classification, etc [1]
- 6. FCM in Principle c as an input parameter segments data into fuzzy clusters by providing typical prototypes for each of them link between objects and cluster prototypes are expressed via a membership matrix where u ij is the membership degree of x i in the cluster j, m is a real number denoting the fuzziness coefficient greater than 1, x i is the i th of d-dimensional data and c j is the cluster centroid of cluster j. Further, fuzzy segmentation is done with the optimization
- 7. Difficulties of Fuzzy c-means clustering as stated by [ 2 ], three major difficulties were drawn ; (1) how to detect optimal number of clusters ? (2) how to choos e the initial cluster centroids ? (3) how to evaluate cluster results, characterized by large variations in cluster shape, cluster density, and the number of points in different clusters
- 8. Solution: FUAT to analyze, explore and visualize different aspects of obtained fuzzy clusters convert black box of fuzzy clustering to transparent box
- 9. FUAT – General Overview FCM and EM based clustering Automatic cluster count estimator for non domain-experts Various interactive viewers for different insights Zooming, filtering, saving is available for results CSV file support R connectivity package (StatConn’s R(D)COM), ZedGraph and Microsoft GLEE is employed during the development Developed at C#.NET
- 10. FUAT General FCM Settings and Membership Table
- 11. FUAT Automatic cluster count detection is based on Bayesian Information Criteria (BIC) implemented in EM framework of Mclust package of R.
- 12. FUAT Cluster Population Distribution Viewer
- 13. FUAT Cluster Centroids Viewer
- 14. FUAT Cluster Membership Histogram Viewer
- 15. FUAT Points of Interest Viewer
- 16. FUAT Cluster Dependency Viewer
- 17. Conclusion FUAT is useful at gaining insight from cluster analysis. Ability for cluster analysis seperately and integrated to overcome difficulties of FCM usage Software R can be used in native applications to power third party ML,DM applications via suitable interfaces. Some Examples of Practical Benefits: Useful at revealing the inner structure of imbalanced data sets Useful at detecting important and dominant attributes in datasets
- 18. References [1] Jingwei Liu, Meizhi Xu, Kernelized fuzzy attribute C-means clustering algorithm, Fuzzy Sets and Systems 159 (2008) 2428 – 2445 [2] Dae-Won Kim , Kwang H. Lee, Doheon Lee, A novel initialization scheme for the fuzzy c-means algorithm for color clustering, Pattern Recognition Letters 25 (2004) 227–237
- 19. Thanks for listening …. Questions?