The notion suggested consists of five elements:
1. The order; i.e., whether the title appears prior to the author in hte text, or vice-versa. In a more general case, where tuples have more than 2 components, the order would be the permutation of components.
2. The URL prefix.
3. The prefix of text, just prior to the first of the title or author.
4. The middle: text appearing between the two data elements.
5. The suffix of text following the second of the two data elements. Both the prefix and suffix were limited to 10 characters.

A pattern makes the difference between data mining on the one hand and simpler statistics on the other.

Whereas the latter usually just AVERAGES ALL data sets, a data mining tool detects PATTERNS which are:
* significantly DIFFERENT from each other
* comprising within one single pattern only data that is very SIMILAR

Example: there is a big birthday and the particpants are analysed. The statistics tool determines that the average age of particpants is 40, the average salary 1500 Euros, gender pretty insignificant. Instead, data mining would e.g. find 3 different patterns: one with average age 67, salary (penion) 1200 Euros, and more females (higher mortality for men in this age group); a second pattern would show average age of 42, salary of 3800 Euros, gender insignificant; and the third pattern has an age of 11. Hence, data mining patterns are by and large much more revealing.

Note: good mining tools work with multi-variate models, and they also determine the most important/ correlated drivers for each pattern.

Data mining is the process of extracting patterns from data. Generally, it is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Example:

For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.