Data Mining

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cut costs, or both.

Data mining tool allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified.

Time Series Algorithm, Decision Tree Algorithm, Clustering Algorithm, Neural Network, Sequence Clustering Algorithm, Naive Bayes, Association Algorithm, Analance BI Integrated



What can Data Mining do

Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations.

Data Mining enables organizations to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits.

Finally, it enables them to "drill down" into summary  information to view detailed transactional data.

Data Mining Algorithms

The key to creating a mining model is the data mining algorithm. The algorithm finds patterns in the data that it passes through, and it translates them into a mining model. Data Mining tool uses the following Data Mining Algorithms

Time Series Algorithm

Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources.

For example, you can use the Time Series algorithm to predict sales and profits based on the historical data in a cube.

Possible Usage :

  • Forecast sales
  • Inventory prediction
  • Web hits prediction
  • Stock value estimation

Decision Tree Algorithm

The Decision Trees algorithm supports both classification and regression and it  works well for predictive modeling.

Using the algorithm, you can predict both discrete and continuous attributes.

Possible Usage:

  • Churn and risk analysis
  • Predict profit or income
Clustering Algorithm

The Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar characteristics.

Using these clusters, you can explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation.

Possible Usage:

  • Customer grouping,
  • Mailing campaign
  • Anomaly detection
Neural Network
The Neural Network algorithm creates classification and regression mining models by constructing a multilayer perception network of neurons.

Possible Usage:

  • Great for finding complicated relationship among attributes
  • For representing patterns detected in the data.
  • For assessing the relationship quality between a customer and a salesperson.
Sequence Clustering Algorithm

The Sequence Clustering algorithm analyzes sequence-oriented data that contains discrete-valued series. Usually the sequence attribute in the series holds a set of events with a specific order (such as a click path). By analyzing the transition between states of the sequence, the algorithm can predict future states in related sequences.

Possible Usage:-

  • Customer behavior
  • Transaction patterns
  • Click stream
  • Customer segmentation
  • Sequence prediction
Naive Bayes

The Naive Bayes algorithm quickly builds mining models that can be used for classification and prediction.

It produces a simple mining model that can be considered a starting point in the data mining process. This makes the model a good option for exploring the data and for discovering how various input attributes are distributed in the different states of the predicted attribute

Possible Usage:

  • Classification
  • Association with multiple predictable attributes
Association Algorithm

The Association algorithm is specifically designed for use in market basket analyses.

The algorithm considers each attribute/value pair (such as product/bicycle) as an item. An itemset is a combination of items in a single transaction. The algorithm scans through the dataset trying to find itemsets that tend to appear in many transactions.

Possible Usage:

  • Market basket analysis
  • Cross selling and recommendations
  • Advanced data exploration
  • Finds frequent itemsets and rules
Analance BI Integrated

Reports created using Data Mining can be published to Analance portal. Once a report is published to a report server, it can be managed, secured and viewed from the Analance portal.