Abstract: A method, system, and computer program product for building a classifier based on the concepts similar to a Bayesian classifier, but using fuzzy theory provides better understanding and an improved approach to modeling the data. The concept of prior information about the class is eliminated, and focuses on the likelihood function (obtained from training data). This is modeled as fuzzy sets. The process of classification uses the possibility-probability transformation. A method of performing data analysis processing includes constructing a fuzzy likelihood by modeling a likelihood of data given a training class using fuzzy sets and transforming the fuzzy likelihood into a probability distribution using a possibility-probability transformation.

Abstract: Improved efficiencies of data mining clustering techniques are provided by preprocessing a sample set of data points taken from a complete data set to provide seeds for centroid calculations of the complete data set. Such seeds are generated by selecting a uniform sample set of data points from a set of multi-dimensional data and then seed values for the cluster determination calculation are determined using a centroid analysis on the sample set of data points. The number of seeds calculated corresponds to a number of data clusters expected in the set of multi-dimensional data points. Seed values are determined using subsample elimination techniques.

Abstract: Improved efficiencies of data mining clustering techniques are provided by preprocessing a sample set of data points taken from a complete data set to provide seeds for centroid calculations of the complete data set. Such seeds are generated by selecting a uniform sample set of data points from a set of multi-dimensional data and then seed values for the cluster determination calculation are determined using a centroid analysis on the sample set of data points. The number of seeds calculated corresponds to a number of data clusters expected in the set of multi-dimensional data points. Seed values are determined using subsample elimination techniques.

Abstract: A method, system, and computer program product for building a classifier based on the concepts similar to a Bayesian classifier, but using fuzzy theory provides better understanding and an improved approach to modeling the data. The concept of prior information about the class is eliminated, and focuses on the likelihood function (obtained from training data). This is modeled as fuzzy sets. The process of classification uses the possibility-probability transformation. A method of performing data analysis processing includes constructing a fuzzy likelihood by modeling a likelihood of data given a training class using fuzzy sets and transforming the fuzzy likelihood into a probability distribution using a possibility-probability transformation.

Abstract: Apriori algorithms are popular data mining techniques for extracting association rules from a body of data. The computational complexity of these algorithms is reduced by representing itemset information at cells of a hypercube. The cells encode associations between the items of each transaction. Direct computation of a cell as a lexicographic combination of items accelerates the computation of itemsets, and thereby improves the computational runtime complexity of the apriori algorithm that discovers association rules. Even faster computation is achieved by a user selected cardinality that limits the maximum size of the itemsets.

Abstract: A decision tree clustering procedure is provided which employs a unified approach to extracting both the decision tree and (preferably fuzzy) clusters. The decision tree is built by subsequent clustering of single dimensions or features, and the choice of the winning separation is based on cluster validity. In one embodiment, the clustering employs a fuzzy c-means (FCM) model and the partition coefficient (PC) to determine the selected separations.

Abstract: This invention relates to a chaos washing machine for improving washing power and preventing the twist of clothes comprising a first washing tank having a plurality of induce holes for inducing the water current in which a detergent dissolved into the space where the laundry is contained, a second washing tank for enclosing the washing tank to be filled with water and a detergent, water current fans and fan motors occurring a turbulent flow to water in which a detergent is dissolved and pushing the turbulent flow from the second washing tank through the induce holes into the first washing tank and a washing tank motor for rotating the first washing tank.