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Last Updated: Sep 2009
Last Updated: Sep 2009
Another is that of a supermarket chain who, through analysis of transactions over a long period of time, found that beer and diapers were often bought together. Although explaining this relationship may be difficult, taking advantage of it is easier, for example by placing the high-profit diapers in the store close to the high-profit beers.
The two examples above deal with association rules within transaction-based data. Not all data is transaction based and logical or inexact rules may also be present within a database. In a manufacturing application, an inexact rule may state that 73% of products which have a specific defect or problem, will develop a secondary problem within the next 6 months.
Although data mining is a relatively new term, the technology is not. Companies for a long time have used powerful computers to sift through volumes of data such as supermarket scanner data, and produce market research reports. Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of analysis.
Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.
The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data mining systems such as neural networks are inherently geared towards prediction rather than knowledge discovery.
The two examples above deal with association rules within transaction-based data. Not all data is transaction based and logical or inexact rules may also be present within a database. In a manufacturing application, an inexact rule may state that 73% of products which have a specific defect or problem, will develop a secondary problem within the next 6 months.
Although data mining is a relatively new term, the technology is not. Companies for a long time have used powerful computers to sift through volumes of data such as supermarket scanner data, and produce market research reports. Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of analysis.
Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.
The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data mining systems such as neural networks are inherently geared towards prediction rather than knowledge discovery.
Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions.
The first and simplest analytical step in data mining is to describe the data; summarize its statistical attributes, visually review it using charts and graphs, and look for potentially meaningful links among variables. Collecting,exploring and selecting the right data are very important.
But data description alone can not provide an action plan. A predictive model based on patterns determined from known results must be build, then the model is tested on results outside the original sample. A good model should never be confused with reality , but it can be a useful guide to understanding a business. The final step is to empirically verify the model.
A simple example of data mining, often called Market Basket Analysis, is its use for retail sales. If a clothing store records the purchases of customers, a data mining system could identify those customers who favour silk shirts over cotton ones.
The first and simplest analytical step in data mining is to describe the data; summarize its statistical attributes, visually review it using charts and graphs, and look for potentially meaningful links among variables. Collecting,exploring and selecting the right data are very important.
But data description alone can not provide an action plan. A predictive model based on patterns determined from known results must be build, then the model is tested on results outside the original sample. A good model should never be confused with reality , but it can be a useful guide to understanding a business. The final step is to empirically verify the model.
A simple example of data mining, often called Market Basket Analysis, is its use for retail sales. If a clothing store records the purchases of customers, a data mining system could identify those customers who favour silk shirts over cotton ones.
