Data Mining Scope and Technique

Data mining is the application of descriptive and predictive analytics to support the marketing, sales and service functions. Although data mining can be performed on operational databases, it is more commonly applied to the more stable datasets held in data marts or warehouses.

Data mining derives its name from the similarities between searching for valuable business information in a large database for example, finding linked products in gigabytes of store scanner data and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides.

Scope

1. Automated prediction of trends and behaviors. Data mining automates the process of find­ing predictive information in large databases. Questions that traditionally required exten­sive hands-on analysis can now be answered directly from the data quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
2. Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern dis­covery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed.

When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions.

Databases can be larger in both depth and breadth:

More columns:

Analysts must often limit the number of variables they examine when doing hands-on analysis due to time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High perfor­mance data mining allows users to explore the full depth of a database, without preselecting a subset of variables.

More rows:

Larger samples yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population. A recent Gartner Group Advanced Technology Research Note listed data mining and artificial intelligence at the top of the five key technology areas that “will clearly have a major impact across a wide range of industries within the next 3 to 5 years.” Gartner also listed parallel architectures and data mining as two of the top 10 new technologies in which companies will invest during the next 5 years.

According to a recent Gartner HPC Research Note, “With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability).”

Data Mining Techniques

The most commonly used techniques in the field include:

• Detection of anomalies: Identifying unusual values in a dataset.
• Dependency modelling: Discovering existing relationships within a dataset. This frequently involves regression analysis.
• Clustering: Identifying structures (clusters) in unstructured data.
• Classification: Generalizing the known structure and applying it to the data.

Techniques in data mining are:

• Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
• Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Clas­sification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID).
• Genetic algorithms: Optimization techniques that use processes such as genetic combina­tion, mutation, and natural selection in a design based on the concepts of evolution.
• Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 31). Sometimes called the k-nearest neighbor technique.
• Rule induction: The extraction of useful if then rules from data based on statistical signifi­cance. Many of these technologies have been in use for more than a decade in specialized analysis tools that work with relatively small volumes of data. These capabilities are now evolving to integrate directly with industry-standard data warehouse and OLAP platforms. The appendix to this white paper provides a glossary of data mining terms.