Data Mining Technology

Extracting knowledge hidden in large volumes of raw data

Competitive advantage requires abilities. Abilities are built through knowledge. Knowledge comes from data. The process of extracting knowledge from data is called Data Mining.

Typical tasks addressed by data mining include:

• Rate customers by their propensity to respond to an offer
• Identify cross-sell opportunities
• Detect fraud and abuse in insurance and finance
• Estimate probability of an illness re-occurrence or hospital re-admission
• Isolate root causes of an outcome in clinical studies
• Determine optimal sets of parameters for a production line operation
• Predict peak load of a network

Without proper analytical tools, discovering useful knowledge hidden in huge volumes of raw data represents a formidable task. The exponential growth in data, diverse nature of data and analysis objectives, the complexity of analyzing mixed structured data and text are among the factors that turn knowledge discovery into a real challenge.

Data Mining provides tools for automated learning from historical data and developing models to predict outcomes of future situations. The best data mining software tools provide a variety of machine learning algorithms for modeling, such as Regression, Neural Network, Decision Tree, Bayesian Network, CHAID, Support Vector Machine, and Random Forest, to name a few.

Yet, data mining requires far more than just machine learning. Data mining additionally involves data pre-processing, and results delivery. Data pre-processing includes loading and integrating data from various data sources, normalizing and cleansing data, and carrying out exploratory data analysis. Results delivery includes model application in production environment and generating reports summarizing the results of the analysis in a simple form for business users.

Megaputer's flagship data mining tool PolyAnalyst supports all steps of data pre-processing and modeling, and results delivering. PolyAnalyst enables you to solve tasks of predicting, classification, clustering, affinity grouping, link analysis, multi-dimensional analysis, and interactive graphical reporting.

Read more about PolyAnalyst capabilities and applications.

Academic Resources

A collection of various academic publications from Megaputer.

• Anomaly Localization - a clustering algorithm with automated attribute selection and invariant to functional coordinate transformations
• Generating Non-linear Functions - PolyAnalyst's data analysis techniques
• Inferring Functional Programs with Machine Learning
• Discovering Numeric Dependencies in the form of Rational Expressions
• Symbolic Knowledge Discovery - introducing PolyAnalyst 2.0, a combination of statistical data preprocessing and symbolic KDD techniques.