Knowledge Discovery and Data Mining in IBM

IBM Research has been at the forefront of this exciting new area from the very beginning. Key advances in robust and scalable data mining, methods for fast pattern detection from very large databases, text and web mining, and innovative business intelligence applications have come from our research laboratories.

DTREG includes Correlation, Factor Analysis and Principle Component Analysis

The process of extracting useful information from a set of data values is called “data mining”. This data can be used to create models to make predictions. Many techniques have been developed for predictive modeling, and there is an art to selecting and applying the best method for a particular situation. DTREG implements the most powerful predictive modeling methods that have been developed. You can use decision tree based methods including TreeBoost and Decision Tree Forests as well as Neural Networks, Support Vector Machine, Gene Expression Programming and Symbolic Regression, K-Means Clustering, Linear Discriminant Analysis, Linear Regression models and Logistic Regression models.

Data Mining using SAS Enterprise Miner

Data Mining using SAS Enterprise miner the reader to a wide variety of data mining techniques in SAS® Enterprise Miner. This first-of-a-kind book explains the purpose and reasoning behind every node that is a part of Enterprise Miner for data mining analysis. Each chapter starts with a short introduction to the assortment of statistics that are generated from the various Enterprise Miner nodes, followed by detailed explanations of configuration settings that are located within each node. The end result of the author’s meticulous presentation is a well crafted study guide on the various methods that one employs to both randomly sample and partition data within the process flow of SAS® Enterprise Miner.

Learning Complex Conditional Probabilities from Data

Naive Bayes is a popular machine learning technique due to its efficiency, direct theoretical foundation and strong classification performance. Our techniques seek to strengthen its accuracy by overcoming the deficiencies of its attribute independence assumption. Averaged One Dependence Estimators (AODE) provides particularly high prediction accuracy with relatively modest computational overheads. Lazy Bayesian Rules (LBR) provides very high prediction accuracy for large training sets, and is computationally efficient when few objects are to be classified for each training set. [AODE, LBR and related papers]

Sterling Commerce adopts UDT

Sterling Commerce, an AT&T Inc (NYSE: T) company, today announced Sterling File Accelerator (SFA). SFA combines the power of the company's Connect:Direct point-to-point file transfer software optimised for high-volume, secure, assured delivery of files with a new UDP Data Transfer-based file transport (UDT) - an application-level data transport protocol that overcomes the latency issues associated with transmission control protocol (TCP)-based transmissions.

Data Mining Research Field

The latest development in data mining, artificial intelligence, analytics, intelligent agents, semiconductors, distributing computing, and network security. SAS, Fair Isaac, Microsoft Analysis Services, SPSS, Cognos, Hyperion, Business Objects, Oracle, Intel, AMD, or Pentaho. Heuristic, Six Sigma, or CMM. Contractor or in-house. Healthcare, Pharmaceutical, Financial, Banking, Biotech, Telecommunications, or Insurance. nihadcuet@gmail.com