Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. According to our observation through this data mining task, we have identified below described functionalities as data mining functionalities.
- Characterization and Discrimination.
- Mining Frequent Patterns, Association Analysis.
- Classification and Prediction.
- Cluster Analysis.
- Outlier Analysis.
- Evolution Analysis.
1. Characterization and Discrimination
In general data can be associated with classes or concepts.
For example, if we take the supermarket system we can have classes like customers, employees, item for sale, etc...
And we can have concepts of customers include big spenders and budget spenders.
* Data characterization is a summarization of the general characteristics or features of a class of data. The data corresponding to the user-specified class are typically collected by a database query.
For example, to study the last month sales of the supermarket can be collected by executing a SQL query. The output of data characterization can be presented in various forms. Such as, pie charts, bar charts, multidimensional tables and etc …
A pie chart
A Bar chart
Example 1
A data mining system should be able to produce a description summarizing of the characteristics of customers who spend more than Rs.1000/= per month in a super market.
We could get the results by executing a SQL query. The result could be a general profile of the customers, such as they are 20-30 years old, employed, and have excellent credit ratings.
* Data discrimination is a comparison of the general features of target class data object with the features of objects from one or a set of contrasting classes.
The target and contrasting classes can be classified by the user and the corresponding data objects retrieved through database query.
For example, Manager of a supermarket likes to compare sales item which one’s sales increased by 10% with which one’s sales decreased by 20%. The forms of output presentation are similar to those for characteristics descriptions, although discrimination descriptions should include comparative measures that help distinguish between the target and contrasting classes.
Example 2
A data mining system should be able to compare two groups of super market customers, such as those who shop regularly versus those who shop rarely.
The resulting description provides a general comparative profile of the customers, such as 80% of the customers who shop regularly have the higher level of income and 10% of the customers who shop rarely have the lower level of income.
2. Mining Frequent Patterns, Association Analysis
Frequent patterns, as the name suggest, are patterns that occur frequently in data.
There are many kind of frequent patterns, including item sets, subsequences, and substructures. A frequent item set typically refers to a set of items that frequently appear together in a transactional data set, such as milk and bread.
A frequently occurring subsequence, such as the pattern that customer tends to purchase first a PC, followed by a web camera, is a frequent sequential pattern.
A substructure can refer to different structural form, such as graphs, trees or lattices, which may be combined with item sets or subsequences.
If a substructure occurs frequently it is called a frequent structured pattern. We use Association analysis to mine these frequent patterns.
Example 3
Suppose as a marketing manager of a supermarket you would like to determine which items are frequently purchased together within the same transactions. An example of such rule, mined from the supermarket database, is
Buys (X,”computer”) -----> buys (X,”software”) [support = 1%, confidence = 50%]
Where X is a variable representing a customer, a confidence 50% means that if a customer buys a computer, there is a 50% chance that he will buy software as well. A 1% support means that 1% of all of the transactions under analysis showed that computer and software were purchased together.
3. Classification and Prediction
Classification is the process of finding a model that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown.
The derived model may be represented in various forms, such as classification (IF-THEN) rules, decision trees, or neural network.
A decision tree is a flow-chart like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and tree leaves represent classes or class distributions.
A neural network, when used for classification, is typically a collection of neuron like processing units with weighted connection between the units.
A decision tree is a flow-chart like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and tree leaves represent classes or class distributions.
A neural network, when used for classification, is typically a collection of neuron like processing units with weighted connection between the units.
Example 4
IF-THEN rules,
Student (class, "undergraduate") AND concentration (level, "high") ==> class A
Student (class,"undergraduate") AND concentration (level,"low") ==> class B
Student (class, "post graduate") ==> class C
Neural network. Decision tree
4. Cluster Analysis
Unlike classification and prediction, which analyze class-labeled data objects, clustering analyzes data objects without consulting a known class label. The objects are clustered based on the principle of maximizing the intra class similarity and minimizing the interclass similarity.
Example 5
Above figure shows four data clusters of customers who visit a particular website according to age and income.
5. Outlier Analysis
A database may contain data objects that do not that do not comply with the general behaviour or model of data. These data objects are outliers. Most data mining method discard outliers as noise or exception. However, in some applications such as fraud detection, the rare event can be more interesting than the regularly occurring ones. The analysis of outlier data is referred to as outlier mining.
Example 6
Outlier analysis may uncover fraudulent usage of credit cards by detecting purchase of extremely large amount for given account number in comparison to regular charges incurred by the same account.
6. Evolution Analysis
Data evolution analysis describes and models regularities or trend for objects whose behaviour changes over time. Although this may include characterization, discrimination or clustering, distinct features such as an analysis include time series data analysis, and similarity-based data analysis.
