what is data mining with examples

          what is data mining with examples

In this article today learn what is data mining with data mining example. and scope of data mining, task of data mining and role of data mining.

What Is Data Mining?

Data mining refers to extracting or mining knowledge from large amounts of data. The term is actually a misnomer. Thus, data mining should have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data.

It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

  The key properties of data mining are

 • Automatic discovery of patterns

 • Prediction of likely outcomes • Creation of actionable information

• Focus on large datasets and databases

The Scope of Data Mining:

Data mining derives its name from the similarities between searching for the 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. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

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. One predictive problem includes forecasting bankruptcy and other forms of default, and identify segments of a population likely to respond similarly to given events.

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.

Tasks of Data Mining

 Data mining involves six common classes of tasks:

• Anomaly detection (Outlier/change/deviation detection) - The identification of

unusual data records, that might be interesting or data errors that require further investigation

Association rule learning (Dependency modeling) –

Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

 Clustering –

is the task of discovering groups and structures in the data that are in some way or another similar", without using known structures in the data.

Classification –

 is the task of generalizing known structures to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

Regression –

attempts to find a function which models the data with the least error

data mining Example:

Consider a marketing head of telecom service provides who wants to increase revenues of long-distance services. For high ROI on his sales and marketing efforts, customer profiling is important. He has a vast data pool of customer information like age, gender, income, credit history, etc. But it is impossible to determine the characteristics of people who prefer long-distance calls with manual analysis. Using data mining techniques, he may uncover patterns between high long-distance call users and their characteristics.

For example, he might learn that his best customers are married females between the age of 45 and 54 who make more than $80,000 per year. Marketing efforts can be targeted to such demographics.

What is the role of data mining?

Data mining is the process of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.