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. the term data mining and knowledge discovery in databases are often used interchangeably to explain the process of extracting hidden information in a large database. Actually the two terms have different concepts but are related to each other. And one of the stages in the whole process of Knowledge Discovery in Database is Data Mining. Knowledge Discovery in Database Process can be explained as follows:
Data Selection, Selection of data from a set of operational data needs to be done before the stage of extracting information in Knowledge Discovery in Database begins. Selected data will be used for data mining process, we choose what kind of data we need for further processing and then the data is stored in a file, separate from the operational database so as to provide convenience for subsequent use.
Pre-processing (Cleaning)
In general, the data obtained, both from the database of a company or experiment has an incomplete stuff like missing data, invalid data or also just a typo. In addition, there are also attributes - data attributes that are not relevant it is also better discarded because of its existence can reduce the quality or accuracy of the results of data mining later. Garbage in garbage out is a term often used to describe this stage. Data cleaning also affects the performance of data mining systems because the data handled will decrease in number and complexity.
Transformation
Some data mining techniques require special data formats before they can be applied. For example, some standard techniques such as association and cluster analysis can only accept categorical input. Hence data in the form of continuous numeric numbers needs to be divided into several intervals. This process is often called binning. Here also conducted data selection required by data mining techniques used. This data transformation and selection also finds the quality of the results from data mining later as there are some characteristics of certain data mining techniques that depend on this resistance.
Data Mining is the process of finding patterns or interesting information in selected data by using a particular technique or method.Data Mining process is the process of finding patterns or interesting information in selected data using a particular technique or method. Techniques, methods, or algorithms in data mining vary widely. The choice of the right method or algorithm depends heavily on the purpose and process of Knowledge Discovery in Database as a whole.
Interpretation/Evaluation, Translation of patterns resulting from data mining.
The pattern of information generated from the data mining process needs to be displayed in a form that is easily understood by interested parties.
This stage is part of the Knowledge Discovery in Database process that includes examining whether the pattern or information found is contrary to previous facts or hypotheses.
Data Selection, Selection of data from a set of operational data needs to be done before the stage of extracting information in Knowledge Discovery in Database begins. Selected data will be used for data mining process, we choose what kind of data we need for further processing and then the data is stored in a file, separate from the operational database so as to provide convenience for subsequent use.
Pre-processing (Cleaning)
In general, the data obtained, both from the database of a company or experiment has an incomplete stuff like missing data, invalid data or also just a typo. In addition, there are also attributes - data attributes that are not relevant it is also better discarded because of its existence can reduce the quality or accuracy of the results of data mining later. Garbage in garbage out is a term often used to describe this stage. Data cleaning also affects the performance of data mining systems because the data handled will decrease in number and complexity.
Transformation
Some data mining techniques require special data formats before they can be applied. For example, some standard techniques such as association and cluster analysis can only accept categorical input. Hence data in the form of continuous numeric numbers needs to be divided into several intervals. This process is often called binning. Here also conducted data selection required by data mining techniques used. This data transformation and selection also finds the quality of the results from data mining later as there are some characteristics of certain data mining techniques that depend on this resistance.
Data Mining is the process of finding patterns or interesting information in selected data by using a particular technique or method.Data Mining process is the process of finding patterns or interesting information in selected data using a particular technique or method. Techniques, methods, or algorithms in data mining vary widely. The choice of the right method or algorithm depends heavily on the purpose and process of Knowledge Discovery in Database as a whole.
Interpretation/Evaluation, Translation of patterns resulting from data mining.
The pattern of information generated from the data mining process needs to be displayed in a form that is easily understood by interested parties.
This stage is part of the Knowledge Discovery in Database process that includes examining whether the pattern or information found is contrary to previous facts or hypotheses.
Advertisement
No comments