Rules-6: A Simple Rule Induction Algorithm for Handling Large Data Sets

Multiresolution Data Handling for Visualization of Very Large Data Sets

Finding Interesting Rules from Large Data Sets

A main goal in data mining is finding those interesting rules from data, which may help the user to do something to her advantage. In order to explore really interesting rules, numerous measures of interestingness and corresponding constraints have been developed based on the structures of rules and the statistic information of data. Given measures and their constraints in a large data set, one...

Packed Rules for Automatic Transfer-rule Induction

We present a method of encoding transfer rules in a highly efficient packed structure using contextualized constraints (Maxwell and Kaplan, 1991), an existing method of encoding adopted from LFG parsing (Kaplan and Bresnan, 1982; Bresnan, 2001; Dalrymple, 2001). The packed representation allows us to encode O(2) transfer rules in a single packed representation only requiring O(n) storage space....

A Genetic Algorithm for Clustering on Very Large Data Sets

Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however,...

A Novel Hyperbolic Smoothing Algorithm for Clustering Large Data Sets

The minimum sum-of-squares clustering problem is considered. The mathematical modeling of this problem leads to a min − sum −min formulation which, in addition to its intrinsic bi-level nature, has the significant characteristic of being strongly nondifferentiable. To overcome these difficulties, the proposed resolution method, called Hyperbolic Smoothing, adopts a smoothing strategy using a sp...