UCI Machine Learning Repository: Computer Hardware Data Set

Computer Hardware Data Set Download: Data Folder, Data Set Description Abstract: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc. Data Set Characteristics:   Multivariate Number of Instances: 209 Area: Computer Attribute Characteristics: Integer Number of Attributes: 9 Date Donated 1987-10-01 Associated Tasks: Regression Missing Values? […]

Computer Hardware Data Set

Download: Data Folder, Data Set Description

Abstract: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.

Data Set Characteristics:  

Multivariate

Number of Instances:

209

Area:

Computer

Attribute Characteristics:

Integer

Number of Attributes:

9

Date Donated

1987-10-01

Associated Tasks:

Regression

Missing Values?

No

Number of Web Hits:

297970

Source:

Creator:

Phillip Ein-Dor and Jacob Feldmesser
Ein-Dor: Faculty of Management
Tel Aviv University; Ramat-Aviv;
Tel Aviv, 69978; Israel

Donor:

David W. Aha (aha ‘@’ ics.uci.edu) (714) 856-8779

Data Set Information:

The estimated relative performance values were estimated by the authors using a linear regression method. See their article (pp 308-313) for more details on how the relative performance values were set.

Attribute Information:

1. vendor name: 30
(adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec,
dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson,
microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry,
sratus, wang)
2. Model Name: many unique symbols
3. MYCT: machine cycle time in nanoseconds (integer)
4. MMIN: minimum main memory in kilobytes (integer)
5. MMAX: maximum main memory in kilobytes (integer)
6. CACH: cache memory in kilobytes (integer)
7. CHMIN: minimum channels in units (integer)
8. CHMAX: maximum channels in units (integer)
9. PRP: published relative performance (integer)
10. ERP: estimated relative performance from the original article (integer)

Relevant Papers:

Ein-Dor and Feldmesser (CACM 4/87, pp 308-317)

Kibler,D. & Aha,D. (1988). Instance-Based Prediction of Real-Valued Attributes. In Proceedings of the CSCSI (Canadian AI) Conference.
[Web Link]

Papers That Cite This Data Set1:

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

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

Citation Request:

Please refer to the Machine Learning
Repository’s citation policy

Source Article

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