A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Clustering. <s> From the Publisher: ::: A reader-friendly introduction to the exciting, vast potential of Genetic Algorithms. ::: The book gives readers a general understanding of the concepts underlying the technology, an insight into its perceived benefits and failings, and a clear and practical illustration of how optimization problems can be solved more efficiently using Falkenauer's new class of algorithms. <s> BIB001 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Clustering. <s> Describes a genetically guided approach to optimizing the hard (J/sub 1/) and fuzzy (J/sub m/) c-means functionals used in cluster analysis. Our experiments show that a genetic algorithm (GA) can ameliorate the difficulty of choosing an initialization for the c-means clustering algorithms. Experiments use six data sets, including the Iris data, magnetic resonance, and color images. The genetic algorithm approach is generally able to find the lowest known J/sub m/ value or a J/sub m/ associated with a partition very similar to that associated with the lowest J/sub m/ value. On data sets with several local extrema, the GA approach always avoids the less desirable solutions. Degenerate partitions are always avoided by the GA approach, which provides an effective method for optimizing clustering models whose objective function can be represented in terms of cluster centers. A series random initializations of fuzzy/hard c-means, where the partition associated with the lowest J/sub m/ value is chosen, can produce an equivalent solution to the genetic guided clustering approach given the same amount of processor time in some domains. <s> BIB002 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Clustering. <s> Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains. <s> BIB003 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Clustering. <s> This paper surveys machine induction techniques for database management and analysis. Our premise is that machine induction facilitates an evolution from relatively unstructured data stores to efficient and correct database implementations. <s> BIB004
As mentioned above, in the classification task the class of a training example is given as input to the data mining algorithm, characterizing a form of supervised learning. In contrast, in the clustering task the data mining algorithm must, in some sense, "discover" classes by itself, by partitioning the examples into clusters, which is a form of unsupervised learning BIB003 , BIB004 . Examples that are similar to each other (i.e. examples with similar attribute values) tend to be assigned to the same cluster, whereas examples different from each other tend to be assigned to distinct clusters. Note that, once the clusters are found, each cluster can be considered as a "class", so that now we can run a classification algorithm on the clustered data, by using the cluster name as a class label. GAs for clustering are discussed e.g. in  , BIB001 , BIB002 .