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How behavioural clusters extracted from the raw data?

Social Physics is a revolutionary new science which uses big data analysis and the mathematical laws of biology to understand the behavior of human crowds

Social Physics is a revolutionary new science which uses big data analysis and the mathematical laws of biology to understand the behavior of human crowds, enabling Endor to overcome traditional Machine Learning limitations and introduce to the world the powerful prediction engine that is able to explain and predict any sort of human behavior, even when the behavior is rapidly changing and evolving.

Learning from numerous orthogonal data sets, the system continuously identifies millions of dynamic behavioral patterns, that trigger binding objects together within the Social Sphere.

All datasets which contain human generated events in time such as: read, view, comment, share, vote, buy, review, tweet and more are all part of the emerging behavioral pattern which assembles the final action of a user.

Social Physics is based on the premise that every event-data representing human activity (e.g. phone call records, credit card purchases, taxi rides, web activity) is guaranteed to contain a special set of human activity patterns that are embedded within that data. These mathematical invariances, which are common to all human data-types across all demographics, can then serve as a filter for detecting emerging behavioral patterns before they can be observed by any other technique.

The engine can run on entirely hashed data without taking advantage of semantics that is embedded in the data. It does it by analyzing the trends that are evolving in time. The time field is the only filed that must not be hashed.

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