Data-Driven Clustering Via Parameterized Lloyd's Families

Authors:
Maria-Florina Balcan Carnegie Mellon University
Travis Dick Carnegie Mellon University
Colin White Carnegie Mellon University

Introduction:

Algorithms for clustering points in metric spaces is a long-studied area of research.

Abstract:

Algorithms for clustering points in metric spaces is a long-studied area of research. Clustering has seen a multitude of work both theoretically, in understanding the approximation guarantees possible for many objective functions such as k-median and k-means clustering, and experimentally, in finding the fastest algorithms and seeding procedures for Lloyd's algorithm. The performance of a given clustering algorithm depends on the specific application at hand, and this may not be known up front. For example, a "typical instance" may vary depending on the application, and different clustering heuristics perform differently depending on the instance.

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