By S. Prabhu, N. Venatesan

ISBN-10: 8122419720

ISBN-13: 9788122419726

ISBN-10: 8122424325

ISBN-13: 9788122424324

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In our example we convert disease yes-no into 1 and 0. In the next stage, we select only those records that have enough information to be of value. Although it is difficult to give detailed rules for this kind of operation, this is a situation that occurs frequently in practice. In most tables that are collected from operational data, a lot of desirable data is missing and most is impossible to retrieve. A general rule states that any detection of data must be a conscious decision, after a thorough analysis of the possible consequences.

One crossover point is chosen at random and parents are divided in that point and parts below crossover point are exchanged to produce new offspring. Parent A Parent B + + x y 3 Offspring x y 2 = x y 2 Fig. 2 Mutation As new individuals are generated, each character is mutated with a given probability. In a binary-coded Genetic Algorithm, mutation may be done by flipping a bit, while in a non-binary-coded GA, mutation involves randomly generating a new character in a specified position. Mutation produces incremental random changes in the offspring generated through crossover.

The minimum percentage of instances in the database that contain all items listed in a given association rule. There are two types of Association rule levels. 1 Rules for Support Level The minimum percentage of instances in the database that contain all items listed in a given association rule. , let T be the set of all “baskets” or “carts” of products bought by the customers from a supermarket – say on a given day. The support of an item set S is the percentage of those transactions in T which contain S.

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Data mining and warehousing by S. Prabhu, N. Venatesan

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