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Neural networks have the remarkable ability to derive meaning from complicated data and can be used to extract patterns and detect trends. These are well suited for continuous valued inputs and outputs. For example handwritten character reorganization, for training a computer to pronounce English, and many real world business problems.

Types of neural networks

Back Propagation

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Regression technique can be adapted for predication while regression analysis can be used to model the relationship between one or more independent variables and dependent variables. In data mining, independent variables are attributes already known and response variables are what we want to predict. The same model types can often be used for both regression and classification.

Types of regression methods

Linear Regression

Multivariate Linear Regression

Nonlinear Regression

Multivariate Nonlinear Regression

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Clustering can be said as identification of similar classes of objects. By using clustering techniques we can identify dense and sparse regions in object space. Classification approach can also be used for effective means of distinguishing groups or classes of object but it becomes costly so clustering is often preferred.

Types of clustering

Partitioning Methods

Hierarchical Agglomerative (divisive) methods

Density based methods

Grid-based methods

Model-based methods

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Classification is the most commonly applied data mining technique to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms.

Types of  classification

Classification by decision tree induction

Bayesian Classification

Neural Networks

Support Vector Machines (SVM)

Classification Based on Associations

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* Retail Industry

Challenges:

Customers had difficulty making hardware and software purchasing decisions, which was hindering online sales.

Results:

Page views increased 67 percent per month after the recommendation engine went live.

Profits tripled in 2001, as sales increased 18 percent versus the same period in the previous year.

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Provident Financial's Home Credit Division., United Kingdom

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Provident Financial's Home Credit Division., United Kingdom

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* Finance Industry and Criminal Investigation

Challenges:

No system to detect and prevent fraud.

Results:

Reduced frequency and magnitude of agent and customer fraud.

Saved money through early fraud detection.

Saved investigator’s time and increased prosecution rate.

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* Telecommunication Industry

Challenges:

Fraudulent activity in telecommunication services.

Results:

Significantly reduced telecommunications fraud for more than 150 telecommunication companies worldwide.

Saved money by enabling real-time fraud detection.

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* Financial Analysis

Challenges:

To reduce direct mail costs.

Increase the efficiency of marketing campaigns.

Increase cross-selling to existing customers, using inbound channels such as the company’s sell center and the internet a one year test of the solution’s effectiveness.

Results:

Provided the marketing team with the ability to predict the effectiveness of its campaigns.

Increased the efficiency of marketing campaign creation, optimization, and execution.

Decreased mailing costs by 35 percent.

Increased conversion rates by 40 percent.

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Patterns are deployed for desired outcome.

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Once data is explored, refined and defined for the specific variables the second step is to form pattern identification. Identify and choose the patterns which make the best prediction.

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In the first step of data exploration data is cleaned and transformed into another form, and important variables and then nature of data based on the problem are determined

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DATA MINING TECHNIQUES AND APPLICATIONS 

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DATA MINING TECHNIQUES AND APPLICATIONS 

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i. FBTO Dutch Insurance Company (Healthcare Provider)

ii. ECtel Ltd., Israel (Cybersecurity)

iii. Provident Financial’s Home Credit Division, United Kingdom (Banking and Financial Services)

iv. Soft map Company Ltd., Tokyo (Advertising and Marketing)

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Knowledge discovery from databases uses a variety of methods and techniques:

i) Classification Technique

ii) Clustering Technique

iii) Regression techniques

iv) Neural networks

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hree steps are involved during the data mining process

Exploration

Pattern identification

Deployment

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Data mining is a logical process that is used to search through a large amounts of data in order to find useful data. The goal of this technique is to find patterns that were previously unknown. Once these patterns are found they can further be used to make certain decisions for the development of their businesses.