By S. Prabhu, N. Venatesan
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These groups are successively combined based on similarity until there is only one group remaining or a specified termination condition is satisfied. For n objects, n-1 mergings are done. Hierarchical algorithms are rigid in that once a merge has been done, it cannot be undone. Although there are smaller computational costs with this, it can also cause problems if an erroneous merge is done. As such, merge points need to be chosen carefully. Here we describe a simple agglomerative clustering algorithm.
Data transformation is separate from data cleansing and data enrichment for data mining purposes because it does not correct existing attribute data or add new attributes, but instead grooms existing attributes for data mining purposes. 5 Coding That data in our example can undergo a number of transformations. First the extra information that was purchased to enrich the database is added to the records describing the individuals. 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.
Error-Prone with Too Many Classes. 2. Computationally Expensive to Train. 3. Trouble with Non-Rectangular Regions. 5 GENETIC ALGORITHM Genetic Algorithms/Evolutionary algorithms are based on Darwin’s theory of survival of the fittest. Here the best program or logic survives from a pool of solutions. Two programs called chromosomes combine to produce a third program called child, the reproduction process goes through Crossover and Mutation operations. 1 Crossover Using simple single point crossover reproduction mechanism, a point along the chromosome length is randomly chosen at which the crossover is done as illustrated.