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Computer Hardware Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with

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Dan Pelleg. Scalable and Practical Probability Density Estimators for Scientific Anomaly Detection. School of Computer Science Carnegie Mellon University. 2004.

of points and linearly with the number of clusters. This allows for clustering with tens of thousands of centroids and millions of points using commodity hardware 7 1.1 Introduction Consider a dataset with R records, each having M attributes. Given a constant k, the clustering problem is to partition the data into k subsets such that each subset behaves "well" under some measure. For example, we

Yongge Wang. A New Approach to Fitting Linear Models in High Dimensional Spaces. Alastair Scott (Department of Statistics, University of Auckland).

The datasets Autos (Automobile), Cpu Computer Hardware /b> , and Cleveland (Heart Disease---Processed Cleveland) 141 Autos Bankbill Bodyfat Cholesterol Cleveland Cpu n / k 159 / 16 71 / 16 252 / 15 297 / 14 297 /

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