Two Medoid-Based Algorithms for Clustering Sets.
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| Názov: | Two Medoid-Based Algorithms for Clustering Sets. |
|---|---|
| Autori: | Nigro, Libero, Fränti, Pasi |
| Zdroj: | Algorithms; Jul2023, Vol. 16 Issue 7, p349, 17p |
| Predmety: | ALGORITHMS, FUZZY algorithms, DIAGNOSIS, DATA analysis, PARALLEL algorithms |
| Abstrakt: | 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 O (N 2) 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. [ABSTRACT FROM AUTHOR] |
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| Databáza: | Complementary Index |
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