Data mining is the process of discovering patterns in large data
sets involving methods at the intersection of artificial intelligence,
machine learning, statistics, and database systems. In other words, data
mining is mining knowledge from data. It uses sophisticated
mathematical algorithms to segment the data and evaluate the probability
of future events.
While large-scale information technology has been evolving separate
transaction and analytical systems, data mining provides the link
between the two. 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. It also able to answer questions that cannot be addressed
through simple query and reporting techniques. Generally, any of four
types of relationships are sought:
- Classes - Stored data is used to locate data in predetermined groups.
- Clusters - Data items are grouped according to logical relationships or consumer preferences.
- Sequential patterns - Data is mined to anticipate behavior patterns and trends.
- Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
Data mining tools and techniques
Data mining techniques are used in many research areas, including
mathematics, cybernetics, genetics and marketing. While data mining
techniques are a means to drive efficiencies and predict customer
behavior, if used correctly, a business can set itself apart from its
competition through the use of predictive analysis.
Web mining - a type of data mining used in customer relationship management,
integrates information gathered by traditional data mining methods and
techniques over the web. Web mining aims to understand customer behavior
and to evaluate how effective a particular website is.
Other data mining techniques include network approaches based on
multitask learning for classifying patterns, ensuring parallel and
scalable execution of data mining algorithms, the mining of large
databases, the handling of relational and complex data types, and
machine learning.
Benefits of data mining
In general, the benefits of data mining come from the ability to uncover
hidden patterns and relationships in data that can be used to make
predictions that impact businesses. Today, data mining is primarily used by companies with a strong consumer
focus - retail, financial, communication, and marketing organizations.
It enables them companies 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.
With data mining, a retailer could use point-of-sale records of customer
purchases to send targeted promotions based on an individual's purchase
history. By mining demographic data from comment or warranty cards, the
retailer could develop products and promotions to appeal to specific
customer segments.
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