Page 39 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 2: Data Mining Concept

evolution and Deviation analysis notes

Evolution and deviation analysis pertain to the study of time related data that changes in time.
Evolution analysis models evolutionary trends in data, which consent to characterising,
comparing, classifying or clustering of time related data. For example, suppose that you have
the major stock market (time-series) data of the last several years available from the New York
Stock Exchange and you would like to invest in shares of high-tech industrial companies. A data
mining study of stock exchange data may identify stock evolution regularities for overall stocks
and for the stocks of particular companies. Such regularities may help predict future trends in
stock market prices, contributing to your decision-making regarding stock investment.
Deviation analysis, on the other hand, considers differences between measured values and
expected values, and attempts to find the cause of the deviations from the anticipated values.
For example, a decrease in total demand of CDs for rent at Video library for the last month, in
comparison to that of the same month of the last year, is a deviation pattern. Having detected a
significant deviation, a data mining system may go further and attempt to explain the detected
pattern (e.g., did the new comedy movies were released last year in comparison to the same
period this year?).

2.8 A Classification of Data Mining Systems

There are many data mining systems available or being developed. Some are specialised systems
dedicated to a given data source or are confined to limited data mining functionalities, other are
more versatile and comprehensive. Data mining systems can be categorised according to various
criteria among other classification are the following:
1. Classification according to the kinds of data source mined: This classification categorises
data mining systems according to the type of data handled such as spatial data, multimedia
data, time-series data, text data, Worldwide Web, etc.
2. Classification according to the data model drawn on: This classification categorises data
mining systems based on the data model involved such as relational database, object-
oriented database, data warehouse, transactional, etc.
3. Classification according to the kind of knowledge discovered: This classification
categorises data mining systems based on the kind of knowledge discovered or data
mining functionalities, such as characterisation, discrimination, association, classification,
clustering, etc. Some systems tend to be comprehensive systems offering several data
mining functionalities together.
4. Classification according to mining techniques used: Data mining systems employ and
provide different techniques. This classification categorises data mining systems according
to the data analysis approach used such as machine learning, neural networks, genetic
algorithms, statistics, visualisation, database-oriented or data warehouse-oriented, etc.
The classification can also take into account the degree of user interaction involved in
the data mining process such as query-driven systems, interactive exploratory systems,
or autonomous systems. A comprehensive system would provide a wide variety of data
mining techniques to fit different situations and options, and offer different degrees of user
interaction.

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