In this paper, an alternative framework for data
analytics is proposed which is based on the spatially-aware
concepts of eccentricity and typicality which represent the
density and proximity in the data space. This approach
is statistical, but differs from the traditional probability
theory which is frequentist in nature. It also differs from the
belief and possibility-based approaches as well as from the
deterministic first principles approaches, although it can be
seen as deterministic in the sense that it provides exactly
the same result for the same data. It also differs from the
subjective expert-based approaches such as fuzzy sets.
It can be used to detect anomalies, faults, form clusters,
classes, predictive models, controllers. The main motivation
for introducing the new typicality- and eccentricity-based
data analytics (TEDA) is the fact that real processes
which are of interest for data analytics, such as climate,
economic and financial, electro-mechanical, biological,
social and psychological etc., are often complex, uncertain
and poorly known, but not purely random. Unlike, purely
random processes, such as throwing dices, tossing coins,
choosing coloured balls from bowls and other games, real
life processes of interest do violate the main assumptions
which the traditional probability theory requires. At the
same time they are seldom deterministic (more precisely,
have always uncertainty/noise component which is nondeterministic),
creating expert and belief-based possibilistic
models is cumbersome and subjective. Despite this,
different groups of researchers and practitioners favour and
do use one of the above approaches with probability theory
being (perhaps) the most widely used one. The proposed
new framework TEDA is a systematic methodology which
does not require prior assumptions and can be used for
development of a range of methods for anomalies and
fault detection, image processing, clustering, classification,
prediction, control, filtering, regression, etc. In this paper
due to the space limitations, only few illustrative examples
are provided aiming proof of concept.
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