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Computer Hardware Data Set Abstract: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc. |
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Data Set Characteristics: |
Multivariate |
Number of Instances: |
209 |
Area: |
Computer |
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Attribute Characteristics: |
Integer |
Number of Attributes: |
9 |
Date Donated |
1987-10-01 |
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Associated Tasks: |
Regression |
Missing Values? |
No |
Number of Web Hits: |
297870 |
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].
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