Fuzzy c-ordered medoids clustering for interval-valued data
Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is...
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| Veröffentlicht in: | Pattern recognition 2016-10, Vol.58, p.49-67 |
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| container_title | Pattern recognition |
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| creator | D׳Urso, Pierpaolo Leski, Jacek M. |
| description | Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is its sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber׳s M-estimators and the Yager׳s Ordered Weighted Averaging (OWA) operators are used in the method proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided.
•Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data.•A new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID) is proposed•The method uses both the Huber׳s M-estimators and the Yager׳s OWA operators to obtain its robustness.•Experiments performed on synthetic data with different types of outliers and a real application are provided. |
| doi_str_mv | 10.1016/j.patcog.2016.04.005 |
| format | Article |
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•Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data.•A new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID) is proposed•The method uses both the Huber׳s M-estimators and the Yager׳s OWA operators to obtain its robustness.•Experiments performed on synthetic data with different types of outliers and a real application are provided.</description><identifier>ISSN: 0031-3203</identifier><identifier>EISSN: 1873-5142</identifier><identifier>DOI: 10.1016/j.patcog.2016.04.005</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Algorithms ; Boundaries ; Clustering ; Fuzzy ; Fuzzy c-ordered medoids clustering ; Huber׳s M-estimators ; Interval-valued data ; Operators ; Ordered weighted averaging ; Outlier interval data ; Outliers (statistics) ; Partitioning ; Pattern recognition ; Robust clustering</subject><ispartof>Pattern recognition, 2016-10, Vol.58, p.49-67</ispartof><rights>2016 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c455t-21cf9fe23c26529ac2e7bf17027001a0726381328ca83f2e96c61a971e58de643</citedby><cites>FETCH-LOGICAL-c455t-21cf9fe23c26529ac2e7bf17027001a0726381328ca83f2e96c61a971e58de643</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.patcog.2016.04.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,46000</link.rule.ids></links><search><creatorcontrib>D׳Urso, Pierpaolo</creatorcontrib><creatorcontrib>Leski, Jacek M.</creatorcontrib><title>Fuzzy c-ordered medoids clustering for interval-valued data</title><title>Pattern recognition</title><description>Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is its sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber׳s M-estimators and the Yager׳s Ordered Weighted Averaging (OWA) operators are used in the method proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided.
•Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data.•A new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID) is proposed•The method uses both the Huber׳s M-estimators and the Yager׳s OWA operators to obtain its robustness.•Experiments performed on synthetic data with different types of outliers and a real application are provided.</description><subject>Algorithms</subject><subject>Boundaries</subject><subject>Clustering</subject><subject>Fuzzy</subject><subject>Fuzzy c-ordered medoids clustering</subject><subject>Huber׳s M-estimators</subject><subject>Interval-valued data</subject><subject>Operators</subject><subject>Ordered weighted averaging</subject><subject>Outlier interval data</subject><subject>Outliers (statistics)</subject><subject>Partitioning</subject><subject>Pattern recognition</subject><subject>Robust clustering</subject><issn>0031-3203</issn><issn>1873-5142</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLAzEQhYMoWKv_wMMevWSdJJvNLoIgxVah4EXPIWYnJWW7W5PdQvvrTVnPHobhMW8eMx8h9wxyBqx83OZ7M9h-k_OkcihyAHlBZqxSgkpW8EsyAxCMCg7imtzEuAVgKg1m5Gk5nk7HzNI-NBiwyXbY9L6JmW3HOGDw3SZzfch8l8TBtDTVmGyNGcwtuXKmjXj31-fka_n6uXij64_V--JlTW0h5UA5s652yIXlpeS1sRzVt2MKuEpnGFC8FBUTvLKmEo5jXdqSmVoxlFWDZSHm5GHK3Yf-Z8Q46J2PFtvWdNiPUbOKy0KlhyBZi8lqQx9jQKf3we9MOGoG-sxKb_XESp9ZaSh0YpXWnqc1TG8cPAYdrcfOYuMD2kEnJP8H_ALR5XMt</recordid><startdate>201610</startdate><enddate>201610</enddate><creator>D׳Urso, Pierpaolo</creator><creator>Leski, Jacek M.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201610</creationdate><title>Fuzzy c-ordered medoids clustering for interval-valued data</title><author>D׳Urso, Pierpaolo ; Leski, Jacek M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c455t-21cf9fe23c26529ac2e7bf17027001a0726381328ca83f2e96c61a971e58de643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Boundaries</topic><topic>Clustering</topic><topic>Fuzzy</topic><topic>Fuzzy c-ordered medoids clustering</topic><topic>Huber׳s M-estimators</topic><topic>Interval-valued data</topic><topic>Operators</topic><topic>Ordered weighted averaging</topic><topic>Outlier interval data</topic><topic>Outliers (statistics)</topic><topic>Partitioning</topic><topic>Pattern recognition</topic><topic>Robust clustering</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>D׳Urso, Pierpaolo</creatorcontrib><creatorcontrib>Leski, Jacek M.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Pattern recognition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>D׳Urso, Pierpaolo</au><au>Leski, Jacek M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy c-ordered medoids clustering for interval-valued data</atitle><jtitle>Pattern recognition</jtitle><date>2016-10</date><risdate>2016</risdate><volume>58</volume><spage>49</spage><epage>67</epage><pages>49-67</pages><issn>0031-3203</issn><eissn>1873-5142</eissn><abstract>Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is its sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber׳s M-estimators and the Yager׳s Ordered Weighted Averaging (OWA) operators are used in the method proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided.
•Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data.•A new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID) is proposed•The method uses both the Huber׳s M-estimators and the Yager׳s OWA operators to obtain its robustness.•Experiments performed on synthetic data with different types of outliers and a real application are provided.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.patcog.2016.04.005</doi><tpages>19</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Pattern recognition, 2016-10, Vol.58, p.49-67 |
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| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Boundaries Clustering Fuzzy Fuzzy c-ordered medoids clustering Huber׳s M-estimators Interval-valued data Operators Ordered weighted averaging Outlier interval data Outliers (statistics) Partitioning Pattern recognition Robust clustering |
| title | Fuzzy c-ordered medoids clustering for interval-valued data |
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