Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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- Bu səhifədəki naviqasiya:
- Association rules
- Table 4.10 Item sets for the weather data with coverage 2 or greater.
4 . 5 M I N I N G A S S O C I AT I O N RU L E S 1 1 3
instances to which the rule applies). This approach is quite infeasible. (Note that, as we mentioned in Section 3.4, what we are calling coverage is often called
Instead, we capitalize on the fact that we are only interested in association rules with high coverage. We ignore, for the moment, the distinction between the left- and right-hand sides of a rule and seek combinations of attribute–value pairs that have a prespecified minimum coverage. These are called item sets: an attribute–value pair is an item. The terminology derives from market basket analysis, in which the items are articles in your shopping cart and the super- market manager is looking for associations among these purchases. Item sets The first column of Table 4.10 shows the individual items for the weather data of Table 1.2, with the number of times each item appears in the dataset given at the right. These are the one-item sets. The next step is to generate the two- item sets by making pairs of one-item ones. Of course, there is no point in generating a set containing two different values of the same attribute (such as outlook = sunny and outlook = overcast), because that cannot occur in any actual instance. Assume that we seek association rules with minimum coverage 2: thus we discard any item sets that cover fewer than two instances. This leaves 47 two- item sets, some of which are shown in the second column along with the number of times they appear. The next step is to generate the three-item sets, of which 39 have a coverage of 2 or greater. There are 6 four-item sets, and no five-item sets—for this data, a five-item set with coverage 2 or greater could only correspond to a repeated instance. The first row of the table, for example, shows that there are five days when outlook = sunny, two of which have temperature = mild, and, in fact, on both of those days humidity = high and play = no as well. Association rules Shortly we will explain how to generate these item sets efficiently. But first let us finish the story. Once all item sets with the required coverage have been gen- erated, the next step is to turn each into a rule, or set of rules, with at least the specified minimum accuracy. Some item sets will produce more than one rule; others will produce none. For example, there is one three-item set with a cov- erage of 4 (row 38 of Table 4.10): humidity = normal, windy = false, play = yes This set leads to seven potential rules: P088407-Ch004.qxd 4/30/05 11:13 AM Page 113
1 1 4 C H A P T E R 4 | A LG O R I T H M S : T H E BA S I C M E T H O D S Table 4.10 Item sets for the weather data with coverage 2 or greater. One-item sets Two-item sets Three-item sets Four-item sets 1 outlook = sunny (5) outlook
= sunny outlook
= sunny outlook
= sunny temperature = mild (2) temperature = hot temperature = hot humidity = high (2) humidity = high play
= no (2) 2 outlook = overcast (4) outlook
= sunny outlook
= sunny outlook
= sunny temperature = hot (2) temperature = hot humidity = high play
= no (2) windy
= false play
= no (2) 3 outlook = rainy (5) outlook
= sunny outlook
= sunny outlook
= overcast humidity = normal (2) humidity = normal temperature = hot play
= yes (2) windy
= false play
= yes (2) 4 temperature = cool (4) outlook
= sunny outlook
= sunny outlook
= rainy humidity = high (3) humidity = high temperature = mild windy
= false (2) windy
= false play
= yes (2) 5 temperature = mild (6) outlook
= sunny outlook
= sunny outlook
= rainy windy
= true (2) humidity = high humidity = normal play
= no (3) windy
= false play
= yes (2) 6 temperature = hot (4) outlook
= sunny outlook
= sunny temperature = cool windy
= false (3) windy
= false humidity = normal play
= no (2) windy
= false play
= yes (2) 7 humidity = normal (7) outlook
= sunny outlook
= overcast play
= yes (2) temperature = hot windy
= false (2) 8 humidity = high (7) outlook
= sunny outlook
= overcast play
= no (3) temperature = hot play
= yes (2) 9 windy = true (6) outlook
= overcast outlook
= overcast temperature = hot (2) humidity = normal play
= yes (2) 10 windy = false (8) outlook
= overcast outlook
= overcast humidity = normal (2) humidity = high play
= yes (2) 11 play = yes (9) outlook
= overcast outlook
= overcast humidity = high (2) windy
= true play
= yes (2) 12 play = no (5) outlook
= overcast outlook
= overcast windy
= true (2) windy
= false play
= yes (2) 13 outlook = overcast outlook
= rainy windy
= false (2) temperature = cool humidity = normal (2) P088407-Ch004.qxd 4/30/05 11:13 AM Page 114 Yüklə 4,3 Mb. Dostları ilə paylaş:
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