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DTSTAMP:20250508T145233Z
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DTSTART;TZID=America/New_York:20250331T183000
DTEND;TZID=America/New_York:20250331T213000
DESCRIPTION:Clustering methods demonstrated their transformative potential 
 across various industries through image segmentation\, anomaly detection\,
  bioinformatics\, and customer segmentation. In this talk\, the speaker wi
 ll explore these techniques in unsupervised machine learning\, focusing on
  foundational clustering algorithms such as K-means\, Hierarchical Cluster
 ing\, and DBSCAN. Through an in-depth analysis of their underlying princip
 les and computational intricacies\, the speaker will highlight how these m
 ethods have evolved to address complex\, high-dimensional data problems. A
 ttendees will learn how K-means remains a versatile tool for partitioning 
 data in linear spaces. The talk will delve into Hierarchical Clustering's 
 unique approach to building dendrograms and capturing multi-scale data rel
 ationships and how DBSCAN's density-based framework reveals clusters amids
 t noise\, making it ideal for discovering patterns in irregular\, real-wor
 ld datasets. The session offers a comprehensive understanding of these alg
 orithms. It equips aspiring data scientists and industry professionals wit
 h the tools to harness the power of clustering for impactful\, data-driven
  decisions.\n\nCo-sponsored by: ANK Zaman\n\nSpeaker(s): Vishnu Pendyala\n
 \nVirtual: https://events.vtools.ieee.org/m/476923
LOCATION:Virtual: https://events.vtools.ieee.org/m/476923
ORGANIZER:azaman@wlu.ca
SEQUENCE:38
SUMMARY:Unveiling the Transformative Power of Unsupervised machine learning
  through Clustering
URL;VALUE=URI:https://events.vtools.ieee.org/m/476923
X-ALT-DESC:Description: <br /><p>Clustering methods demonstrated their tran
 sformative potential across various industries through image segmentation\
 , anomaly detection\, bioinformatics\, and customer segmentation. In this 
 talk\, the speaker will explore these techniques in unsupervised machine l
 earning\, focusing on foundational clustering algorithms such as K-means\,
  Hierarchical Clustering\, and DBSCAN. Through an in-depth analysis of the
 ir underlying principles and computational intricacies\, the speaker will 
 highlight how these methods have evolved to address complex\, high-dimensi
 onal data problems. Attendees will learn how K-means remains a versatile t
 ool for partitioning data in linear spaces. The talk will delve into Hiera
 rchical Clustering's unique approach to building dendrograms and capturing
  multi-scale data relationships and how DBSCAN's density-based framework r
 eveals clusters amidst noise\, making it ideal for discovering patterns in
  irregular\, real-world datasets. The session offers a comprehensive under
 standing of these algorithms. It equips aspiring data scientists and indus
 try professionals with the tools to harness the power of clustering for im
 pactful\, data-driven decisions.</p>
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