Kinds and Applications of Data Mining

Let us understand data mining and its applications in real world.

“ Data mining ” is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

Kinds of Data Mining

1. Graph mining

2. Spatial data mining

3. Multimedia data mining

4. Text mining

5. Multi-relational data mining

6. Genetic Sequence data mining

7. Transaction data mining

8. Sequential data mining

9. Temporal data mining

10. Document data mining

Data Mining Applications

a) Financial Data Analysis:

1. Design and construction of data warehouses for multidimensional data analysis and data mining:

Data-warehouses need to be constructed for banking and financial data. For example, one may like to view the debt and revenue changes by month, by region, or by sector with the maximum, minimum, average, trend, and other statistical information.

2. Loan payment prediction and customer credit policy analysis.

3. Classification and clustering of customers for targeted marketing:

Identifying the most crucial factors influencing customer’s decisions regarding banking.

b) Retail Industry:

1. Customer retention:

It can be done by sequential pattern mining which investigates changes in customer consumption/loyalty and suggests adjustments on pricing.

2. Cross-Sell:

Selling an additional product to an existing customer. For example, on amazon, if we buy a laptop then they suggest us to buy a laptop bag by giving some offer so that the customer buys 2 products instead of one. The products that they suggest will be related to the product that the customer bought.

3. Personalized Recommendation:

Customer’s purchase behavior data both online and offline will be collected and they recommend the related items for the customers. In amazon, eBay they collect the data on what customer has browsed on websites and apps and based on what the customer wants they try to give recommendations.

c) Telecommunication Industry:

1. Fraudulent pattern analysis and identification of unusual patterns:

Identifies potentially fraudulent users and their typical usage patterns. Detects attempts to gain fraudulent entry to customer accounts.

2. Mobile telecommunication services:

Unusually busy mobile phone traffic at certain locations may indicate something abnormal.

d) Biological Data Analysis:

1. Bioinformatics:

It is a promising young field that applies computer technology in molecular biology and develops algorithms and methods to manage and analyze biological data.

2. DNA, RNA, and proteins:

They contain internally the various chemical components in a sequence. In this, we can mine the sequence of the patterns of the chemical composition of DNA, RNA, and proteins or the genes.

e) Scientific Application:

1. Scientific domains like Geosciences, Astronomy, Meteorology.

2. Chemical Engineering, Fluid Dynamics, Structural Mechanics.

f) Intrusion Detection:

1. Analysis of Stream Data:

An event may be normal on its own but considered malicious if viewed as a sequence of events. Thus, it is necessary to find sequential patterns and identify outliers.

2. Signature-based detection:

This method of detection utilizes signatures, which are attack patterns that are preconfigured and predetermined by domain experts.

3. Anomaly-based detection:

This method builds models of normal network behavior (called profiles) that are then used to detect new patterns that significantly deviate from the profiles. Such deviations may represent actual intrusions or simply be new behaviors that need to be added to the profiles. The main advantage of anomaly detection is that it may detect novel intrusions that have not yet been observed.

These are some of the real world applications of data mining.