source: orange/orange/doc/datasets/lenses.htm @ 1760:9d4bb141fb0e

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data info file

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1<html>
2<head>
3<title>Lenses Data Base</title>
4</head>
5<body>
6<h1>Info on Lenses Data Base</h1>
7<pre>
81. Title: Database for fitting contact lenses
9
102. Sources:
11     (a) Cendrowska, J. "PRISM: An algorithm for inducing modular rules",
12         International Journal of Man-Machine Studies, 1987, 27, 349-370
13     (b) Donor: Benoit Julien (Julien@ce.cmu.edu)
14     (c) Date: 1 August 1990
15
163. Past Usage:
17      1. See above.
18      2. Witten, I. H. & MacDonald, B. A. (1988). Using concept
19         learning for knowledge acquisition. International Journal of
20         Man-Machine Studies, 27, (pp. 349-370).
21
22 Notes:  This database is complete (all possible combinations of
23         attribute-value pairs are represented).
24
25         Each instance is complete and correct.
26
27         9 rules cover the training set.
28
294. Relevant Information Paragraph:
30    The examples are complete and noise free.
31    The examples highly simplified the problem. The attributes do not
32    fully describe all the factors affecting the decision as to which type,
33    if any, to fit.
34
355. Number of Instances: 24
36
376. Number of Attributes: 4 (all nominal)
38
397. Attribute Information:
40    -- 3 Classes
41     1 : the patient should be fitted with hard contact lenses,
42     2 : the patient should be fitted with soft contact lenses,
43     1 : the patient should not be fitted with contact lenses.
44
45    1. age of the patient: (1) young, (2) pre-presbyopic, (3) presbyopic
46    2. spectacle prescription:  (1) myope, (2) hypermetrope
47    3. astigmatic:     (1) no, (2) yes
48    4. tear production rate:  (1) reduced, (2) normal
49
508. Number of Missing Attribute Values:   0
51
529. Class Distribution:
53   1. hard contact lenses: 4
54   2. soft contact lenses: 5
55   3. no contact lenses: 15
56</pre>
57</body>
58</html>
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