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School of
Computer Science
and Communication

Clustering Course Lectures

This is the preliminary content of the lectures:

Day 1

  • Introduction
    • KTH CSC, Language Technology Group
    • Introduction to Clustering
  • Course administration
    • Course Plan and Content
    • Two Laborations
      • Requierments
      • Reports
    • Individual Project
      • Definitions due
      • Presentation on third day
      • Report due
    • Literature
    • Date for third day
  • Information Retrieval
    • Representation
    • Similarity
  • Three algorithms: K-Means, Agglomerative, Divisive
  • Evaluation
  • Presentation
    • Textual
    • Visual
  • Applications
    • Search Engines
    • Text Mining and Hypotheses
  • Introduction to Laboration 1
  • Laboration 1 (work)

Day 2

  • Course Administration
    • Individual Project
  • More Applications, Algorithms, ...
    • Social Network Analysis
    • Feature Selection/Extraction
  • Dimension Reduction
    • Clustering
    • LSA
    • Random Indexing
  • Spectral Clustering

Day 3

Presentations of individual projects.

^ Up to Clustering Course.

Published by: Magnus Rosell <rosell@nada.kth.se>
Updated 2008-10-13