Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering
<p dir="ltr">Clustering has proved to be a successful classification method when it comes to dealing with multiview data. Each method and technique tries to achieve efficiency and accuracy in classifying the multiview data. Multi-source data contains noise and divergence. Another pro...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513541642387456 |
|---|---|
| author | Saadia Jamil (22045946) |
| author2 | Eid Rehman (22045949) Tariq Shahzad (21632645) Muhammad Ishtiaq (509503) Tehseen Mazhar (19332663) Yazeed Yasin Ghadi (22045952) Arfan Ahmed (17541309) |
| author2_role | author author author author author author |
| author_facet | Saadia Jamil (22045946) Eid Rehman (22045949) Tariq Shahzad (21632645) Muhammad Ishtiaq (509503) Tehseen Mazhar (19332663) Yazeed Yasin Ghadi (22045952) Arfan Ahmed (17541309) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saadia Jamil (22045946) Eid Rehman (22045949) Tariq Shahzad (21632645) Muhammad Ishtiaq (509503) Tehseen Mazhar (19332663) Yazeed Yasin Ghadi (22045952) Arfan Ahmed (17541309) |
| dc.date.none.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3412950 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multi_Self-Organizing_Map_SOM_Pipeline_Architecture_for_Multi-View_Clustering/29899151 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Self organizing map multiview clustering classification pipeline Clustering methods Noise measurement Clustering algorithms Computer science Self-organizing feature maps Dimensionality reduction Classification algorithms |
| dc.title.none.fl_str_mv | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Clustering has proved to be a successful classification method when it comes to dealing with multiview data. Each method and technique tries to achieve efficiency and accuracy in classifying the multiview data. Multi-source data contains noise and divergence. Another problem is that each view contains many features, so usually the multiview dataset is multi-dimensional. This raises basic problems like the need for a dimensionality reduction technique for optimal selection of features, fusing the data of different views, and maintaining the inter- and intra-consensus of the multiview dataset. The fusion technique should merge the complementary information efficiently. The goal of this study is to use a promising technique for dimension reduction that reduces the noise but maintains the inter-view and cross-view consensus. A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. It calculates the dimension relevance with various data instances. These further place the relevant dimension samples in one group. The method does not need to know the number of clusters before hand, as it dynamically determines it during the process. Finally, this study proposed a novel multi-view learning framework that analyzes multi-source data and generates fine clusters efficiently.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3412950" target="_blank">https://dx.doi.org/10.1109/access.2024.3412950</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_67d9a36f6cd9bc6fcdd6fa95d6e84722 |
| identifier_str_mv | 10.1109/access.2024.3412950 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29899151 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View ClusteringSaadia Jamil (22045946)Eid Rehman (22045949)Tariq Shahzad (21632645)Muhammad Ishtiaq (509503)Tehseen Mazhar (19332663)Yazeed Yasin Ghadi (22045952)Arfan Ahmed (17541309)Information and computing sciencesArtificial intelligenceMachine learningSelf organizing mapmultiviewclusteringclassificationpipelineClustering methodsNoise measurementClustering algorithmsComputer scienceSelf-organizing feature mapsDimensionality reductionClassification algorithms<p dir="ltr">Clustering has proved to be a successful classification method when it comes to dealing with multiview data. Each method and technique tries to achieve efficiency and accuracy in classifying the multiview data. Multi-source data contains noise and divergence. Another problem is that each view contains many features, so usually the multiview dataset is multi-dimensional. This raises basic problems like the need for a dimensionality reduction technique for optimal selection of features, fusing the data of different views, and maintaining the inter- and intra-consensus of the multiview dataset. The fusion technique should merge the complementary information efficiently. The goal of this study is to use a promising technique for dimension reduction that reduces the noise but maintains the inter-view and cross-view consensus. A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. It calculates the dimension relevance with various data instances. These further place the relevant dimension samples in one group. The method does not need to know the number of clusters before hand, as it dynamically determines it during the process. Finally, this study proposed a novel multi-view learning framework that analyzes multi-source data and generates fine clusters efficiently.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3412950" target="_blank">https://dx.doi.org/10.1109/access.2024.3412950</a></p>2024-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3412950https://figshare.com/articles/journal_contribution/Multi_Self-Organizing_Map_SOM_Pipeline_Architecture_for_Multi-View_Clustering/29899151CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298991512024-01-01T00:00:00Z |
| spellingShingle | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering Saadia Jamil (22045946) Information and computing sciences Artificial intelligence Machine learning Self organizing map multiview clustering classification pipeline Clustering methods Noise measurement Clustering algorithms Computer science Self-organizing feature maps Dimensionality reduction Classification algorithms |
| status_str | publishedVersion |
| title | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| title_full | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| title_fullStr | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| title_full_unstemmed | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| title_short | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| title_sort | Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering |
| topic | Information and computing sciences Artificial intelligence Machine learning Self organizing map multiview clustering classification pipeline Clustering methods Noise measurement Clustering algorithms Computer science Self-organizing feature maps Dimensionality reduction Classification algorithms |