An overview of cluster analysis techniques from a data mining point of view is given in this document. This is done by a strict separation of the questions of various similarity and distance measures and related optimization criteria for clustering techniques from the methods to create and modify the clusters themselves. In addition to this general overview, the second focus is laid on a discussion of the essential ingredients of the demographic cluster algorithm in the IBM Intelligent Miner products,

We present parallel algorithms for building decision-tree classifiers on shared-memory multi- processor (SMP) systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task pipelining and dynamic load balancing to yield faster implementations. The task parallel approach uses dynamic subtree partitioning among processors. We evaluate the performance of these algorithms on two machi

This paper presents a new predictive modeling algorithm that draws inspiration from the Kolmogorov superposition theorem. An initial version of the algorithm is presented that combines gradient boosting with decisiontree methods to construct models that have the same overall mathematical structure as Kolmogorov’s superposition equation. Improvements to the algorithm are then presented that significantly increase its rate of convergence. The resulting algorithm, dubbed “transform regression

The scientific paper attached to this note contains a description of the A-Priori associations mining algorithm. This algorithm is used by the mining component of DWE if an associations model is built and the algorithm 'A-Priori' is chosen. The paper considers the problem of mining association rules on a shared-nothing multiprocessor. It presents three algorithms that explore a spectum of trade-offs between computation, memory usage, synchronization, and the use of problem-specific information. The best a