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:
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.
A collection of various academic publications from Megaputer.
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