Data Mining is a term used to describe the discovery of knowledge in a database. Data mining is a process that uses statistical, mathematical, artificial intelligence and machine learning techniques to extract and identify useful information and related knowledge from large databases. Data Mining by Garnet Group is a process of finding relationships that are meaningful, patterns, trends by checking with a large set of data stored in storage using pattern recognition techniques such as static and mathematical techniques.
Data mining is divided into several groups based on the tasks that can be done, namely:
Description, Sometimes researchers and analysts simply want to try to figure out how to describe the patterns and trends contained in the data. For example, the polling officers may not be able to find out the information or the fact that who is not professional enough will be little supported in the presidential election. Descriptions of patterns and tendencies often provide an explanation for a pattern and a tendency.
Estimate, Almost the same as the classification, unless the target variable estimates more toward numerical than toward the category. The model is built using a complete record that provides the value of the predicted variable.
Prediction is almost the same as the classification and estimate, noting that in predicting the value of the results will exist in the future. Examples of predictions in business:
Classification, There are target categories of variables. For example, income classification can be separated into three categories: high income, medium income, low income.
Clustering, Represents the grouping of records, observing or noting and forming classes of objects that have similarities with each other and have an unlike the records in other clusters.
Associations, Association tasks in Data Mining are finding attributes that appear at a time. In the business world more commonly called shopping cart analysis.
Data mining is divided into several groups based on the tasks that can be done, namely:
Description, Sometimes researchers and analysts simply want to try to figure out how to describe the patterns and trends contained in the data. For example, the polling officers may not be able to find out the information or the fact that who is not professional enough will be little supported in the presidential election. Descriptions of patterns and tendencies often provide an explanation for a pattern and a tendency.
Estimate, Almost the same as the classification, unless the target variable estimates more toward numerical than toward the category. The model is built using a complete record that provides the value of the predicted variable.
Prediction is almost the same as the classification and estimate, noting that in predicting the value of the results will exist in the future. Examples of predictions in business:
- Predicted raw material prices in the next three months
- The predicted percentage increase in traffic accidents next year if the lower limit of speed is raised.
Classification, There are target categories of variables. For example, income classification can be separated into three categories: high income, medium income, low income.
Clustering, Represents the grouping of records, observing or noting and forming classes of objects that have similarities with each other and have an unlike the records in other clusters.
Associations, Association tasks in Data Mining are finding attributes that appear at a time. In the business world more commonly called shopping cart analysis.
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