Online Data Research/ Data Mining

There is a vast resource of information on web which can be of good use to you. We make this information available to you in timely and most cost effective manner by utilizing our special research methodology.
We have a dedicated team of experienced web researchers who can accomplish any complex web based research tasks related to any knowledge field with high reliability and accuracy. More....

KNOWLEDGE DISCOVERY AND DATA MINING at WPI

The common themes of the research projects in our group are data mining and knowledge discovery in databases. Knowledge discovery is the process of finding general patterns/principles that summarize/explain a set of "observations". Very large databases have become the standard, making it impossible for human beings to mine the data "by hand" looking for interesting patterns. Automated tools are therefore needed to help during the extraction of these patterns. Examples of application domains include astronomical data from the Hubble telescope, data on consumer preferences obtained by credit card companies, medical histories, genomic data, web usage data, etc. More...

ELDER RESEARCH, Inc. Data Mining & Pattern Discovery

ELDER RESEARCH is a leader in data mining consulting and data mining training. Drawing from theory and experience in diverse professional fields, ER brings cutting-edge research into front-line practice to achieve competitive advantages through expert Data Mining. More...

Data Mining Research with the LSST

The LSST catalog database will exceed 10 petabytes, comprising several hundred attributes for 5 billion galaxies, 10 billion stars, and over 1 billion variable sources (optical variables, transients, or moving objects), extracted from over 20,000 square degrees of deep imaging in 5 passbands with thorough time domain coverage: 1000 visits over the 10-year LSST survey lifetime. The opportunities are enormous for novel scientific discoveries within this rich time-domain ultra-deep multi-band survey database. Data Mining, Machine Learning, and Knowledge Discovery research opportunities with the LSST are now under study, with a potential for new collaborations to develop to contribute to these investigations. More....

RULEQUEST Research

RULEQUEST research provides you some data mining tools. Their data mining tools are used in over 50 countries.

FDK (From Data to Knowledge)

This is one of famous research group. Enjoy this post.

The project develops methods and tools for analyzing large data sets and for searching for unexpected relationships in the data. The project combines development of combinatorial pattern matching algorithms with statistical techniques and database methods. The resulting techniques typically search through a large collection of potential local models that describe some aspect of the data in an easily understandable way. The project has also studied the construction of efficient predictors from large masses of data.

Statistics and Data Mining Research in Bell Lab

Statistics research at Bell Labs is part of Mathematical Sciences research, and also part of Computer Science research via a "dotted-line" relation. Our tradition of fundamental research driven by real-world applications goes back to Walter Shewhart and John Tukey. Today, by continuing to focus on data from a host of challenging applications, we are working on new ways to think about, look at, and compute with data.

Welcome to the Spatial Database and Spatial Data Mining Research Group

Hi every body!

This post will helpful for those who interested in spatial database at the same spatial data mining.

Our work is focused on the storage, management and analysis of scientific and geographic data, information and knowledge. The research is motivated by and has been applied to application areas such as transportation, virtual environments, Earth science, epidemiology, and cartography.

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