Data mining




Data Mining leads to discovery of underlying trends, patterns and associations between different objects, or relationships between different observations made for all objects. It can be used for association, characterization, classification, clustering, pattern recognition and many more.


Access to quality and representative data is by far more important than the quantity of data collected. Hence a carefully planned data collection strategy normally pays off well, even in the case of observational studies. Experimental design can reduce cost by ensuring that the largest amount of information gets captured for a minimum number of observations and should be used whenever possible.


Even a novice data scientist can extract powerful business insights and build reliable classification models from datasets that represents a large enough spread in variations, regardless if there was a relative small number of objects recorded.

Intelligent Considerations


Meaningful data mining has a lot to do with visualization of the raw data in a manner that creates a picture to the eye. Mere numbers on a page is hard to interpret but a good picture can paint a thousand words.


Our software solutions has extensive graphic capabilities for visualizing, grouping and categorizing data.


It goes on to provide several different types of graphs and diagnostic plots for finding and interpreting true outliers within a dataset.


Another big advantage is the easy import and manipulation of datasets from a wide range of formats.

Powerful Visualization Capabilities


The Unscrambler® has an extensive range of plots and graphs for visualizing data, diagnosing and interpreting results from data mining.

Timmerman Analytical

PO Box 4002

Old Oak


South Africa


Phone: +27-82-335-9752

Fax: +27-86-540-4051

E-mail: info@idatascience.co.za

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