Two Medoid-Based Algorithms for Clustering Sets
This paper proposes two algorithms for clustering data, which are variable-sized sets of elementary items. An example of such data occurs in the analysis of a medical diagnosis, where the goal is to detect human subjects who share common diseases to possibly predict future illnesses from previous me...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/7/349 |
| _version_ | 1797590611627343872 |
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| author | Libero Nigro Pasi Fränti |
| author_facet | Libero Nigro Pasi Fränti |
| author_sort | Libero Nigro |
| collection | DOAJ |
| description | This paper proposes two algorithms for clustering data, which are variable-sized sets of elementary items. An example of such data occurs in the analysis of a medical diagnosis, where the goal is to detect human subjects who share common diseases to possibly predict future illnesses from previous medical history. The first proposed algorithm is based on K-medoids and the second algorithm extends the random swap algorithm, which has proven to be capable of efficient and careful clustering; both algorithms depend on a distance function among data objects (sets), which can use application-sensitive weights or priorities. The proposed distance function makes it possible to exploit several seeding methods that can improve clustering accuracy. A key factor in the two algorithms is their parallel implementation in Java, based on functional programming using streams and lambda expressions. The use of parallelism smooths out the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo stretchy="false">(</mo><msup><mi>N</mi><mn>2</mn></msup><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula> computational cost behind K-medoids and clustering indexes such as the Silhouette index and allows for the handling of non-trivial datasets. This paper applies the algorithms to several benchmark case studies of sets and demonstrates how accurate and time-efficient clustering solutions can be achieved. |
| first_indexed | 2024-03-11T01:22:57Z |
| format | Article |
| id | doaj.art-d66fce0d1ccb4d26a4293168067aaafb |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T01:22:57Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d66fce0d1ccb4d26a4293168067aaafb2023-11-18T17:59:24ZengMDPI AGAlgorithms1999-48932023-07-0116734910.3390/a16070349Two Medoid-Based Algorithms for Clustering SetsLibero Nigro0Pasi Fränti1Engineering Department of Informatics Modelling Electronics and Systems Science, University of Calabria, 87036 Rende, ItalySchool of Computing, Machine Learning Group, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, FinlandThis paper proposes two algorithms for clustering data, which are variable-sized sets of elementary items. An example of such data occurs in the analysis of a medical diagnosis, where the goal is to detect human subjects who share common diseases to possibly predict future illnesses from previous medical history. The first proposed algorithm is based on K-medoids and the second algorithm extends the random swap algorithm, which has proven to be capable of efficient and careful clustering; both algorithms depend on a distance function among data objects (sets), which can use application-sensitive weights or priorities. The proposed distance function makes it possible to exploit several seeding methods that can improve clustering accuracy. A key factor in the two algorithms is their parallel implementation in Java, based on functional programming using streams and lambda expressions. The use of parallelism smooths out the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo stretchy="false">(</mo><msup><mi>N</mi><mn>2</mn></msup><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula> computational cost behind K-medoids and clustering indexes such as the Silhouette index and allows for the handling of non-trivial datasets. This paper applies the algorithms to several benchmark case studies of sets and demonstrates how accurate and time-efficient clustering solutions can be achieved.https://www.mdpi.com/1999-4893/16/7/349unsupervised clusteringK-meansK-medoidsrandom swapseeding methodsclustering sets |
| spellingShingle | Libero Nigro Pasi Fränti Two Medoid-Based Algorithms for Clustering Sets Algorithms unsupervised clustering K-means K-medoids random swap seeding methods clustering sets |
| title | Two Medoid-Based Algorithms for Clustering Sets |
| title_full | Two Medoid-Based Algorithms for Clustering Sets |
| title_fullStr | Two Medoid-Based Algorithms for Clustering Sets |
| title_full_unstemmed | Two Medoid-Based Algorithms for Clustering Sets |
| title_short | Two Medoid-Based Algorithms for Clustering Sets |
| title_sort | two medoid based algorithms for clustering sets |
| topic | unsupervised clustering K-means K-medoids random swap seeding methods clustering sets |
| url | https://www.mdpi.com/1999-4893/16/7/349 |
| work_keys_str_mv | AT liberonigro twomedoidbasedalgorithmsforclusteringsets AT pasifranti twomedoidbasedalgorithmsforclusteringsets |