Focus on large data sets and databases Data mining can answer questions that cannot be addressed through simple query and reporting techniques. Automatic Discovery Data mining is accomplished by building models.
Feature selection refers to the The features of data mining of reducing the inputs for processing and analysis, or of finding the most meaningful inputs. A related term, feature engineering or feature extractionrefers to the process of extracting useful information or features from existing data.
Why Do Feature Selection? Feature selection is critical to building a good model for several reasons. One is that feature selection implies some degree of cardinality reduction, to impose a cutoff on the number of attributes that can be considered when building a model.
Data almost always contains more information than is needed to build the model, or the wrong kind of information. For example, you might have a dataset with columns that describe the characteristics of customers; however, if the data in some of the columns is very sparse you would gain very little benefit from adding them to the model, and if some of the columns duplicate each other, using both columns could affect the model.
Not only does feature selection improve the quality of the model, it also makes the process of modeling more efficient. If you use unneeded columns while building a model, more CPU and memory are required during the training process, and more storage space is required for the completed model.
Even if resources were not an issue, you would still want to perform feature selection and identify the best columns, because unneeded columns can degrade the quality of the model in several ways: Noisy or redundant data makes it more difficult to discover meaningful patterns.
If the data set is high-dimensional, most data mining algorithms require a much larger training data set. During the process of feature selection, either the analyst or the modeling tool or algorithm actively selects or discards attributes based on their usefulness for analysis.
The analyst might perform feature engineering to add features, and remove or modify existing data, while the machine learning algorithm typically scores columns and validates their usefulness in the model.
In short, feature selection helps solve two problems: Your goal in feature selection should be to identify the minimum number of columns from the data source that are significant in building a model. With some algorithms, feature selection techniques are "built-in" so that irrelevant columns are excluded and the best features are automatically discovered.
Each algorithm has its own set of default techniques for intelligently applying feature reduction. However, you can also manually set parameters to influence feature selection behavior.
During automatic feature selection, a score is calculated for each attribute, and only the attributes that have the best scores are selected for the model. You can also adjust the threshold for the top scores.
SQL Server Data Mining provides multiple methods for calculating these scores, and the exact method that is applied in any model depends on these factors: The algorithm used in your model The data type of the attribute Any parameters that you may have set on your model Feature selection is applied to inputs, predictable attributes, or to states in a column.
When scoring for feature selection is complete, only the attributes and states that the algorithm selects are included in the model-building process and can be used for prediction. If you choose a predictable attribute that does not meet the threshold for feature selection the attribute can still be used for prediction, but the predictions will be based solely on the global statistics that exist in the model.
Note Feature selection affects only the columns that are used in the model, and has no effect on storage of the mining structure.
One feature that is vital for a successful data mining system yet is often overlooked is the need to make the data “over-the-counter” in that the data’s viewers are assisted in easily understanding the data and using it correctly (just as an over-. Feature Selection (Data Mining) 05/08/; 9 minutes to read Contributors. In this article. APPLIES TO: SQL Server Analysis Services Azure Analysis Services Feature selection is an important part of machine learning. Feature selection refers to the process of reducing the inputs for processing and analysis, or of finding the most meaningful inputs. Data mining algorithms are often sensitive to specific characteristics of the data: outliers (data values that are very different from the typical values in your database), irrelevant columns, columns that vary together (such as age and date of birth), data coding, and data that you choose to include or exclude.
The columns that you leave out of the mining model are still available in the structure, and data in the mining structure columns will be cached. The specific method used in any particular algorithm or data set depends on the data types, and the column usage. The interestingness score is used to rank and sort attributes in columns that contain nonbinary continuous numeric data.
Shannon's entropy and two Bayesian scores are available for columns that contain discrete and discretized data. However, if the model contains any continuous columns, the interestingness score will be used to assess all input columns, to ensure consistency.Data is a cornerstone of smart decisions in today’s business world and companies need to utilize the appropriate data mining tools to quickly discover insights from their data.
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
One feature that is vital for a successful data mining system yet is often overlooked is the need to make the data “over-the-counter” in that the data’s viewers are assisted in easily understanding the data and using it correctly (just as an over-.
One feature that is vital for a successful data mining system yet is often overlooked is the need to make the data “over-the-counter” in that the data’s viewers are assisted in easily understanding the data and using it correctly (just as an over-the-counter product must offer labeling and other features to ensure its contents are used correctly).
And at the end of this discussion about the data mining methodology, one can clearly understand the feature, elements, purpose, characteristics and benefits with its own limitations.
Therefore, after reading all the above mentioned information about the data mining techniques, one can determine its credibility and feasibility even better. Data mining analysis is performed by using properties of the focus of analysis. Such properties can be the unique property of a focus component.
Sometimes they can also be properties of a level higher than the focus component level.