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How Data Mining works

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Christian Hissibini

I am a Tech enthusiast who loves to blend Dev & Design on Web and Mobile Platforms. I am also a Windows Platform Dev MVP


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How Data Mining works

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Data mining, as a composite discipline, represents a variety of methods or techniques used in different analytic capabilities that address a gamut of organizational needs, ask different types of questions and use varying levels of human input or rules to arrive at a decision.

 

Descriptive Modeling

It uncovers shared similarities or groupings in historical data to determine reasons behind success or failure, such as categorizing customers by product preferences or sentiment. Sample techniques include:

Clustering
Grouping similar records together.
Anomaly detection
Identifying multidimensional outliers.
Association rule learning
Detecting relationships between records.
Principal component analysis
Detecting relationships between variables.
Affinity grouping
Grouping people with common interests or similar goals (e.g., people who buy X often buy Y and possibly Z).

 

Predictive Modeling

This modeling goes deeper to classify events in the future or estimate unknown outcomes – for example, using credit scoring to determine an individual’s likelihood of repaying a loan. Predictive modeling also helps uncover insights for things like customer churn, campaign response or credit defaults. Sample techniques include:

Regression
A measure of the strength of the relationship between one dependent variable and a series of independent variables.
Neural networks
Computer programs that detect patterns, make predictions and learn.
Decision trees
Tree-shaped diagrams in which each branch represents a probable occurrence.
Support vector machines
Supervised learning models with associated learning algorithms.

 

 

Prescriptive Modeling

With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. You need the ability to successfully parse, filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy.

In the end, you should not look at data mining as a separate, standalone entity because pre-processing (data preparation, data exploration) and post-processing (model validation, scoring, model performance monitoring) are equally essential. Prescriptive modelling looks at internal and external variables and constraints to recommend one or more courses of action – for example, determining the best marketing offer to send to each customer. Sample techniques include:

Predictive analytics plus rules
Developing if/then rules from patterns and predicting outcomes.
Marketing optimization
Simulating the most advantageous media mix in real time for the highest possible ROI.

 

 

 

Ref
https://msdn.microsoft.com/ – https://blogs.nvidia.com –  https://www.sas.com/en_us/insights/analytics/machine-learning.html

profile

Christian Hissibini

I am a Tech enthusiast who loves to blend Dev & Design on Web and Mobile Platforms. I am also a Windows Platform Dev MVP

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