Data Mining and Clustering

• In today’s world as data volumes grow almost exponentially being able to unlock new value or deliver tangible competitive advantage from your data is beyond the capability of most companies. – Intellify changes that for ever by combining leading complex clustering, pattern recognition and complex event processing technologies into a single easy to use product!

  Clustering

  Clustering is a data mining technology, which solves classification problems. Its object is to distribute cases (people, objects , events etc.) into groups, so that the degree of association to be strong between members of the same cluster and weak between members of different clusters. This way each cluster describes, in terms of data collected, the class to which its members belong. Clustering is discovery tool. It may reveal associations and structure in data which, though not previously evident, nevertheless are sensible and useful once found. This can then be further used to deliver valuable insights that can be presented or further processed.

  Pattern Recognition

  Pattern Recognition is one of the key technologies in the data-mining field that is used to search for hidden groups or patterns in large volumes of data. Applying this technology allow us to uncover hidden patterns in the data or unlock potentially very valuable intelligence that can be further processed within Intellify.

  Complex Event Processing

  Complex Event Processing, or CEP, is technology to process events and discover complex patterns among multiple streams of event data. Using Intellify™’s modelling layer we can represent data to act as input for clustering or classification algorithms. This allows us to identify patterns of behavior which can be extracted and further processed through one of Intellify™’s other technologies without exiting the application.

  Correlation

  Correlation and dependence are any of a broad class of statistical relationships between two or more random variables or observed data values.Correlations are useful because they can indicate a predictive relationship that can be exploited in practice, such as the change in demand for a product against it’s price or promotion.