Data Mining Techniques That Will Deliver Great Results
Website:https://www.datanami.com/2017/06/14/8-concrete-data-mining-techniques-will-deliver-best-results/
As information technology expands, the need for data mining grows. Data mining is not that easy. Below are a few steps which will help you deliver the best results possible.
Recognizing and Handling Incomplete Data
Data mining is useless when there is incomplete data involved. It is therefore important to analyze and determine the missing data pattern. For example, determining whether the data is completely missed, randomly missing or whether or not there is a particular variable missing. Imputation can be used to put substitute data in the place of the missing data. Some examples of imputation include average imputation and regression substitution.
Clustering Techniques
These techniques are old but still very useful. Clustering data refers to the process by which data is analyzed based on behavior. Data which possesses behavior that is similar is clustered together. Data that displays similar behavior is said to exhibit the same prediction value.
Anomaly Detection
Removing anomalies allows for the best data mining results. These occur when information that doesn't fit a pattern is captured. They come under names such as exceptions and outliers. Anomalies are usually identified when the data set looks nothing like what you expected it to. This requires extra analysis to properly predict what would happen with the data.
Decision Trees Used for Exploration and Data Pre-Processing
Decision trees are considered to be the newest form of data mining technology. They help the individual to analyze and identify the important parts of the database. Financial institutions may find decision trees very useful in solving credit card attrition predictions. Decision trees are also helpful in pre-processing data for other kinds of prediction algorithms.
As information technology expands, the need for data mining grows. Data mining is not that easy. Below are a few steps which will help you deliver the best results possible.
Recognizing and Handling Incomplete Data
Data mining is useless when there is incomplete data involved. It is therefore important to analyze and determine the missing data pattern. For example, determining whether the data is completely missed, randomly missing or whether or not there is a particular variable missing. Imputation can be used to put substitute data in the place of the missing data. Some examples of imputation include average imputation and regression substitution.
Clustering Techniques
These techniques are old but still very useful. Clustering data refers to the process by which data is analyzed based on behavior. Data which possesses behavior that is similar is clustered together. Data that displays similar behavior is said to exhibit the same prediction value.
Anomaly Detection
Removing anomalies allows for the best data mining results. These occur when information that doesn't fit a pattern is captured. They come under names such as exceptions and outliers. Anomalies are usually identified when the data set looks nothing like what you expected it to. This requires extra analysis to properly predict what would happen with the data.
Decision Trees Used for Exploration and Data Pre-Processing
Decision trees are considered to be the newest form of data mining technology. They help the individual to analyze and identify the important parts of the database. Financial institutions may find decision trees very useful in solving credit card attrition predictions. Decision trees are also helpful in pre-processing data for other kinds of prediction algorithms.
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