Robust local triangular kernel density-based clustering for high-dimensional data
A number of clustering algorithms can be employed to find clusters in multivariate data. However, the effectiveness and efficiency of the existing algorithms are limited, since the respective data has high dimension, contain large amount of noise and consist of clusters with arbitrary shapes and den...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2013
|
| Subjects: |
| _version_ | 1796859613019111424 |
|---|---|
| author | Musdholifah, Aina Mohd Hashim, Siti Zaiton |
| author_facet | Musdholifah, Aina Mohd Hashim, Siti Zaiton |
| author_sort | Musdholifah, Aina |
| collection | ePrints |
| description | A number of clustering algorithms can be employed to find clusters in multivariate data. However, the effectiveness and efficiency of the existing algorithms are limited, since the respective data has high dimension, contain large amount of noise and consist of clusters with arbitrary shapes and densities. In this paper, a new kernel density-based clustering algorithm, called Local Triangular Kernel-based Clustering (LTKC), is proposed to deal with these conditions. LTKC is based on combination of k-nearest-neighbor density estimation and triangular kernel density-based clustering. The advantages of our LTKC approach are: (1) it has a firm mathematical basis; (2) it requires only one parameter, number of neighbors; (3) it defines the number of cluster automatically; (4) it allows discovering clusters with arbitrary shapes and densities; and (5) it is significantly faster than existing algorithms. LTKC is tested using artificial data and applied to some UCI data. A comparison with k-means, KFCM and well known density-based clustering algorithms including ILGC, DBSCAN, and DENCLUE shows the superiority of our proposed LTKC algorithm. |
| first_indexed | 2024-03-05T19:29:45Z |
| format | Conference or Workshop Item |
| id | utm.eprints-51289 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:29:45Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | utm.eprints-512892017-07-18T07:46:37Z http://eprints.utm.my/51289/ Robust local triangular kernel density-based clustering for high-dimensional data Musdholifah, Aina Mohd Hashim, Siti Zaiton QA75 Electronic computers. Computer science A number of clustering algorithms can be employed to find clusters in multivariate data. However, the effectiveness and efficiency of the existing algorithms are limited, since the respective data has high dimension, contain large amount of noise and consist of clusters with arbitrary shapes and densities. In this paper, a new kernel density-based clustering algorithm, called Local Triangular Kernel-based Clustering (LTKC), is proposed to deal with these conditions. LTKC is based on combination of k-nearest-neighbor density estimation and triangular kernel density-based clustering. The advantages of our LTKC approach are: (1) it has a firm mathematical basis; (2) it requires only one parameter, number of neighbors; (3) it defines the number of cluster automatically; (4) it allows discovering clusters with arbitrary shapes and densities; and (5) it is significantly faster than existing algorithms. LTKC is tested using artificial data and applied to some UCI data. A comparison with k-means, KFCM and well known density-based clustering algorithms including ILGC, DBSCAN, and DENCLUE shows the superiority of our proposed LTKC algorithm. 2013 Conference or Workshop Item PeerReviewed Musdholifah, Aina and Mohd Hashim, Siti Zaiton (2013) Robust local triangular kernel density-based clustering for high-dimensional data. In: 2013 5th International Conference on Computer Science and Information Technology, CSIT 2013 - Proceedings, MAR 27-28, 2013, Amman, Jordon. http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=11&SID=R2Cjh3fA6kIeWhVr585&page=1&doc=1 |
| spellingShingle | QA75 Electronic computers. Computer science Musdholifah, Aina Mohd Hashim, Siti Zaiton Robust local triangular kernel density-based clustering for high-dimensional data |
| title | Robust local triangular kernel density-based clustering for high-dimensional data |
| title_full | Robust local triangular kernel density-based clustering for high-dimensional data |
| title_fullStr | Robust local triangular kernel density-based clustering for high-dimensional data |
| title_full_unstemmed | Robust local triangular kernel density-based clustering for high-dimensional data |
| title_short | Robust local triangular kernel density-based clustering for high-dimensional data |
| title_sort | robust local triangular kernel density based clustering for high dimensional data |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT musdholifahaina robustlocaltriangularkerneldensitybasedclusteringforhighdimensionaldata AT mohdhashimsitizaiton robustlocaltriangularkerneldensitybasedclusteringforhighdimensionaldata |