[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123449-en":3,"doc-seo-123449-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123449,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",6,"Technology","Unveiling the Transformative Power of Unsupervised machine learning through Clustering - Overview","Presentation on how streaming platforms and other domains group similar items using unsupervised machine learning with clustering. It introduces features as vectors, explains similarity via distances in feature space, and contrasts clusters versus manually labeled classes. The talk outlines key hyperparameters such as similarity metrics and clustering types, surveys common algorithms including K-Means, hierarchical, and DBSCAN, and connects the clustering goal to real-world examples like political parties, neighborhoods, and biological species.","4/19/25  \nUnveiling the Transformative Power of Unsupervised machine learning through Clustering  \nVishnu S. Pendyala, Ph.D.  \nSan Jose State University  \nTo cite this presentation: Pendyala, V.S. (2025)“Unveiling the Transformative Power of Unsupervised machine learning through Clustering”. IEEE Computer Society, Kitchener-Waterloo Chapter Technical Talk, March 31, 2025  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \nHow do Streaming services know that these movies can be grouped together?  \nFeatures!  \nThis Photo by Unknown Author is licensed under CC BY-SA-NC  \n4/19/25  \nFeatures are expressed as vectors  \n\n| Feature | Value |\n| --- | --- |\n| Genre (Action) | 8 |\n| Genre (Drama) | 7 |\n| Genre (War) | 6 |\n| Historical Accuracy | 7 |\n| Heroism Level | 9 |\n| Number of Battles | 5 |\n| Emotional Depth | 8 |\n| IMDB User Rating | 8.4 |\n\nFeature vector 􀀡 ! = [8, 7, 6, 7, 9, 5, 8, 8.4]  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \n| Clustering relies on distances in the feature space |\n| --- |\n| May not correspond to\u003Cbr>physical distance  |\n| Distance is a measure for\u003Cbr>similarity  |\n| Smaller distance => better similarity  |\n| Inter-cluster distances must be maximized |\n| Intra-cluster distances must be minimized |\n| No labels (unsupervised) |\n| Labels => classification |\n\nThis Photo by Unknown Author is licensed under CC BY-SA-NC  \n4/19/25  \nSome applications of Clustering: Social Media  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \nMany applications of clustering  \n4/19/25  \nImage Clustering  \nClasses vs  \nClusters  \nClasses are defined and data labeled manually (Supervised)  \nClusters are deduced automatically, no labels (Unsupervised)  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons AttributionNoDerivatives 4.0 International License  \n4/19/25  \nHyperparameters: What can you choose for clustering data?  \nSimilarity metric: Cosine, Euclidean, Manhattan, Geodesic,…  \nType of clustering: partitioning (non-overlapping subsets), Hierarchical Clustering (tree-like), Density-Based , Fuzzy Clustering (points to belong to multiple clusters with varying degrees of membership),…  \nClustering algorithm: K-Means, Agglomerative Clustering, Divisive Clustering, DBSCAN,…  \nNumber of clusters – some algorithms like K-Means need this  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \nHow can we detect and form these clusters of data points programmatically?  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \n4/19/25  \nHow are clusters formed in real life?  \nPolitical Parties  \nP1 is most  \nP2 is most representative of ideology 2  \nrepresentative of ideology 1  \nCloser to P1 than to P2 in terms of ideology  \nCloser to P2 than to P1 in terms of ideology  \n©Vishnu S. Pendyala This work is licensed under a  \nCreative Commons Attribution-NoDerivatives 4.0 International License  \nHow are clusters formed in real life?  \nNeighborhoods  \nCommunity ABC Community XYZ  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \n4/19/25  \nHow are clusters formed in real life?  \nBiological Species  \nThis Photo by Unknown Author is licensed under CC BY-SA   \nClustering algorithms for today  \nK-MEANS  \nHIERARCHICAL CLUSTERING  \nDENSITY-BASED  \nSPATIAL CLUSTERING OF APPLICATIONS  \nWITH NOISE  \n(DBSCAN)  \n©Vishnu S. Pendyala This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License  \n4/19/25  \nThe Goal of Clustering  \nEntities across the clusters must be as dissimilar in the feature space as  \npossible   \nEntities within the clusters must be as  \nsimilar in the feature space as ","cbCaitMIvdCfowMv","https://ap.wps.com/l/cbCaitMIvdCfowMv","pdf",16444234,1,44,"English","en",105,"# Features and Feature Vectors\n## Representing features as vectors\n## Distance in feature space and similarity\n# Clustering Fundamentals\n## No labels vs classification\n## Inter-cluster and intra-cluster objectives\n# Applications and Examples\n## Social media clustering\n## Image clustering\n## Real-life formation: parties, neighborhoods, species\n# Hyperparameters for Clustering\n## Similarity metrics\n## Clustering types\n## Clustering algorithms\n# K-Means Clustering\n## Intuition and centroids","[{\"question\":\"How does unsupervised clustering group similar movies or items?\",\"answer\":\"Clustering uses feature representations expressed as vectors and groups entities based on similarity in the feature space using distances. Smaller distances indicate better similarity, and clusters are optimized so intra-cluster items are similar while inter-cluster items are dissimilar.\"},{\"question\":\"What is the difference between classes and clusters?\",\"answer\":\"Classes are defined with manually assigned labels in supervised data, while clusters are deduced automatically in unsupervised settings without labels. Labels typically correspond to classification rather than clustering.\"},{\"question\":\"What hyperparameters can be chosen when performing clustering?\",\"answer\":\"You can choose a similarity metric (e.g., cosine, Euclidean, Manhattan, geodesic), a clustering type (partitioning, hierarchical, density-based, fuzzy), and the clustering algorithm (e.g., K-Means, agglomerative, divisive, DBSCAN). The number of clusters may also be required by some algorithms like K-Means.\"}]","Unveiling the Transformative Power of Unsupervised machine learning through Clustering - Overview | PDF",1785816586,111,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"unveiling-the-transformative-power-of-unsupervised-machine-learning-through-clustering-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/unveiling-the-transformative-power-of-unsupervised-machine-learning-through-clustering-overview/123449/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does unsupervised clustering group similar movies or items?","Question",{"text":75,"@type":76},"Clustering uses feature representations expressed as vectors and groups entities based on similarity in the feature space using distances. Smaller distances indicate better similarity, and clusters are optimized so intra-cluster items are similar while inter-cluster items are dissimilar.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the difference between classes and clusters?",{"text":80,"@type":76},"Classes are defined with manually assigned labels in supervised data, while clusters are deduced automatically in unsupervised settings without labels. Labels typically correspond to classification rather than clustering.",{"name":82,"@type":73,"acceptedAnswer":83},"What hyperparameters can be chosen when performing clustering?",{"text":84,"@type":76},"You can choose a similarity metric (e.g., cosine, Euclidean, Manhattan, geodesic), a clustering type (partitioning, hierarchical, density-based, fuzzy), and the clustering algorithm (e.g., K-Means, agglomerative, divisive, DBSCAN). 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