How many clusters exist? Answer via maximum clustering similarity implemented in R
Finding the number of clusters in a data set is considered as one of the fundamental problems in cluster analysis. This paper integrates maximum clustering similarity (MCS), for finding the optimal number of clusters, into R©statistical software through the package MCSim. The similarity between the...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| منشور في: |
2019
|
| الوصول للمادة أونلاين: | https://doi.org/10.1080/24709360.2019.1615770 https://dspace.auk.edu.kw/handle/11675/5752 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1870679717417320448 |
|---|---|
| author | Zogheib, Bashar |
| author_facet | Zogheib, Bashar |
| author_role | author |
| dc.creator.none.fl_str_mv | Zogheib, Bashar |
| dc.date.none.fl_str_mv | 2019 2020-04-10T14:16:58Z 2020-04-10T14:16:58Z |
| dc.identifier.none.fl_str_mv | https://doi.org/10.1080/24709360.2019.1615770 https://dspace.auk.edu.kw/handle/11675/5752 |
| dc.publisher.none.fl_str_mv | Taylor & Francis |
| dc.relation.none.fl_str_mv | Biostatistics and Epidemiology |
| dc.title.none.fl_str_mv | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| dc.type.none.fl_str_mv | Journal Article info:eu-repo/semantics/publishedVersion |
| description | Finding the number of clusters in a data set is considered as one of the fundamental problems in cluster analysis. This paper integrates maximum clustering similarity (MCS), for finding the optimal number of clusters, into R©statistical software through the package MCSim. The similarity between the two clustering methods is calculated at the same number of clusters, using Rand [Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971;66:846–850.] and Jaccard [The distribution of the flora of the alpine zone. New Phytologist. 1912;11:37–50.] indices, corrected for chance agreement. The number of clusters at which the index attains its maximum with most frequency is a candidate for the optimal number of clusters. Unlike other criteria, MCS can be used with circular data. Seven clustering algorithms, existing in R©, are implemented in MCSim. A graph of the number of clusters vs. clusters similarity using corrected similarity indices is produced. Values of the similarity indices and a clustering tree (dendrogram) are produced. Several examples including simulated, real, and circular data sets are presented to show how MCSim successfully works in practice. |
| id | AUKR_5baaa5e3fc75df6d7e80388d73f5db3b |
| network_acronym_str | AUKR |
| network_name_str | AU Kuwait Rep |
| oai_identifier_str | oai:dspace.auk.edu.kw:11675/5752 |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Taylor & Francis |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | How many clusters exist? Answer via maximum clustering similarity implemented in RZogheib, BasharFinding the number of clusters in a data set is considered as one of the fundamental problems in cluster analysis. This paper integrates maximum clustering similarity (MCS), for finding the optimal number of clusters, into R©statistical software through the package MCSim. The similarity between the two clustering methods is calculated at the same number of clusters, using Rand [Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971;66:846–850.] and Jaccard [The distribution of the flora of the alpine zone. New Phytologist. 1912;11:37–50.] indices, corrected for chance agreement. The number of clusters at which the index attains its maximum with most frequency is a candidate for the optimal number of clusters. Unlike other criteria, MCS can be used with circular data. Seven clustering algorithms, existing in R©, are implemented in MCSim. A graph of the number of clusters vs. clusters similarity using corrected similarity indices is produced. Values of the similarity indices and a clustering tree (dendrogram) are produced. Several examples including simulated, real, and circular data sets are presented to show how MCSim successfully works in practice.Taylor & Francis2020-04-10T14:16:58Z2020-04-10T14:16:58Z2019Journal Articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1080/24709360.2019.1615770https://dspace.auk.edu.kw/handle/11675/5752Biostatistics and Epidemiologyoai:dspace.auk.edu.kw:11675/57522022-01-13T09:22:00Z |
| spellingShingle | How many clusters exist? Answer via maximum clustering similarity implemented in R Zogheib, Bashar |
| status_str | publishedVersion |
| title | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| title_full | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| title_fullStr | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| title_full_unstemmed | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| title_short | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| title_sort | How many clusters exist? Answer via maximum clustering similarity implemented in R |
| url | https://doi.org/10.1080/24709360.2019.1615770 https://dspace.auk.edu.kw/handle/11675/5752 |