Page 147 - DCAP208_Management Support Systems

Management Support Systems

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Example: After starting a credit policy, the OurVideoStore managers could analyze the
customers behaviours vis-a-vis their credit, and label accordingly the customers who received
credits with three possible labels “safe”, “risky” and “very risky”.
The classification analysis would generate a model that could be used to either accept or
reject credit requests in the future.
Prediction: Prediction has attracted considerable attention given the potential implications
of successful forecasting in a business context. There are two major types of predictions:
one can either try to predict some unavailable data values or pending trends, or predict a
class label for some data. The latter is tied to classification. Once a classification model is
built based on a training set, the class label of an object can be foreseen based on the
attribute values of the object and the attribute values of the classes. Prediction is however
more often referred to the forecast of missing numerical values, or increase/decrease
trends in time related data. The major idea is to use a large number of past values to
consider probable future values.

Clustering: Similar to classification, clustering is the organization of data in classes. However,
unlike classification, in clustering, class labels are unknown and it is up to the clustering
algorithm to discover acceptable classes. Clustering is also called unsupervised classification,
because the classification is not dictated by given class labels. There are many clustering
approaches all based on the principle of maximizing the similarity between objects in a
same class (intra-class similarity) and minimizing the similarity between objects of different
classes (inter-class similarity).
Outlier analysis: Outliers are data elements that cannot be grouped in a given class or
cluster. Also known as exceptions or surprises, they are often very important to identify.
While outliers can be considered noise and discarded in some applications, they can
reveal important knowledge in other domains, and thus can be very significant and their
analysis valuable.
Evolution and deviation analysis: 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 characterizing, comparing, classifying or clustering of time
related data. 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.
It is common that users do not have a clear idea of the kind of patterns they can discover
or need to discover from the data at hand. It is therefore important to have a versatile and
inclusive data mining system that allows the discovery of different kinds of knowledge
and at different levels of abstraction. This also makes interactivity an important attribute
of a data mining system.

Task Compare and contrast characterization and discrimination.
9.1.5 Working of Data Mining

How exactly is data mining able to tell you important things that you didn’t know or what is
going to happen next? The technique that is used to perform these feats in data mining is called
modeling. Modeling is simply the act of building a model in one situation where you know the
answer and then applying it to another situation that you don’t. For instance, if you were

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