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Greedy Clustering Methods and Their Applications in Data Analysis

This lecture by Prof. Ramin Zabih introduces greedy clustering methods, emphasizing maximizing similarity within clusters while minimizing it between clusters. Key topics include the assumptions in clustering, the representation of clusters, and various types of clustering algorithms. The session covers applications in economics, market segmentation, spatial clustering, epidemic detection, and document classification. The greedy approach is highlighted, detailing how methods like K-center clustering optimize cluster centers effectively, often yielding near-optimal solutions that are valuable in computer science research.

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Greedy Clustering Methods and Their Applications in Data Analysis

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  1. Greedy clustering methods Prof. Ramin Zabih http://cs100r.cs.cornell.edu

  2. Administrivia • Guest lecture on Tuesday • Prof. Charlie van Loan, on ellipse fitting • Prelim 3: Thursday 11/29 (last lecture) • Review session the day before • A6 is due Friday 11/30 (LDOC) • Final projects due Friday 12/7 • Course evals! http://www.engineering.cornell.edu/courseeval/

  3. Clustering • Generally speaking, the goal is to maximize similarity within a cluster, and to minimize similarity between clusters • We will assume that the space and the number of clusters are known • Usually (but not always) a safe assumption • Sometimes you only know distances between pairs of points • May not be embeddable in a space • Often assume a known number of clusters

  4. Example Figure from Johan Everts

  5. Applications of clustering • Economics or politics • Finding similar-minded or similar behaving groups of people (market segmentation) • Find stocks that behave similarly • Spatial clustering • Earthquake centers cluster along faults • Find epidemics (famous example: cholera) • Classify documents for web search • Automatic directory construction (like Yahoo!) • Find more documents “like this one”

  6. Clustering algorithms • There are many types of methods • How is a cluster is represented? • Is a cluster represented by a data point, or by a point in the middle of the cluster? • This is similar to an argument we saw before… • An interesting class of methods uses graph partitioning • Edge weights are distances • There are many classes of algorithms

  7. Greedy methods • Many CS problems can be solved by repeatedly doing whatever seems best at the moment • I.e., without needing a long-term plan • These are called greedy algorithms • Example: hill climbing for convex function minimization • Example: sorting by swapping out-of-order pairs

  8. K-center clustering • Find K cluster centers that minimize the maximum distance between any point and its nearest center • We want the worst point in the worst cluster to still be good (i.e., close to its center) • This is a nice optimization problem • Given a clustering, we can describe how good it is, so we have a figure of merit • But to find the optimal clustering we have to try every one!

  9. A greedy method • Pick a random point to start with, this is your first cluster center • Find the farthest point from the cluster center, this is a new cluster center • Find the farthest point from any cluster center and add it

  10. Example Figure from Jia Li

  11. An amazing property • This algorithm gives you a figure of merit that is no worse than twice the optimum • Such results are very difficult to achieve, and the subject of much research • Mostly in CS681, a bit in CS482 • You can’t find the optimum, yet you can prove something about it!

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