Difference Between Descriptive and Predictive Data Mining
Last Updated : 21 Feb, 2023
Descriptive mining:
This term is basically used to produce correlation, cross-tabulation, frequency etc. These technologies are used to determine the similarities in the data and to find existing patterns. One more application of descriptive analysis is to develop the captivating subgroups in the major part of the data available. This analytics emphasis on the summarization and transformation of the data into meaningful information for reporting and monitoring.
Examples of descriptive data mining include clustering, association rule mining, and anomaly detection. Clustering involves grouping similar objects together, while association rule mining involves identifying relationships between different items in a dataset. Anomaly detection involves identifying unusual patterns or outliers in the data.
Predictive Data Mining:
The main goal of this mining is to say something about future results not of current behaviour. It uses the supervised learning functions which are used to predict the target value. The methods come under this type of mining category are called classification, time-series analysis and regression. Modelling of data is the necessity of the predictive analysis, and it works by utilizing a few variables of the present to predict the future not known data values for other variables.
Examples of predictive data mining include regression analysis, decision trees, and neural networks. Regression analysis involves predicting a continuous outcome variable based on one or more predictor variables. Decision trees involve building a tree-like model to make predictions based on a set of rules. Neural networks involve building a model based on the structure of the human brain to make predictions.
The main differences between descriptive and predictive data mining are:
Purpose: Descriptive data mining is used to describe the data and identify patterns and relationships. Predictive data mining is used to make predictions about future events.
Approach: Descriptive data mining involves analyzing historical data to identify patterns and relationships. Predictive data mining involves using statistical models and machine learning algorithms to identify patterns and relationships that can be used to make predictions.
Output: Descriptive data mining produces summaries and visualizations of the data. Predictive data mining produces models that can be used to make predictions.
Timeframe: Descriptive data mining is focused on analyzing historical data. Predictive data mining is focused on making predictions about future events.
Applications: Descriptive data mining is used in applications such as market segmentation, customer profiling, and product recommendation. Predictive data mining is used in applications such as fraud detection, risk assessment, and demand forecasting.
Difference Between Descriptive and Predictive Data Mining:
| S.No. | Comparison | Descriptive Data Mining | Predictive Data Mining |
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| 1. | Basic | It determines, what happened in the past by analyzing stored data. | It determines, what can happen in the future with the help past data analysis. |
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| 2. | Preciseness | It provides accurate data. | It produces results does not ensure accuracy. |
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| 3. | Practical analysis methods | Standard reporting, query/drill down and ad-hoc reporting. | Predictive modelling, forecasting, simulation and alerts. |
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| 4. | Require | It requires data aggregation and data mining | It requires statistics and forecasting methods |
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| 5. | Type of approach | Reactive approach | Proactive approach |
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| 6. | Describe | Describes the characteristics of the data in a target data set. | Carry out the induction over the current and past data so that predictions can be made. |
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| 7. | Methods(in general) | - what happened?
- where exactly is the problem?
- what is the frequency of the problem?
| - what will happen next?
- what is the outcome if these trends continue?
- what actions are required to be taken?
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Conclusion:
In conclusion, descriptive and predictive data mining are two important techniques for discovering patterns and trends in large datasets. Descriptive data mining is used to summarize and describe the data, while predictive data mining is used to make predictions about future events. Both techniques have their own advantages and applications, and the choice of technique depends on the specific problem and the nature of the data.
Frequently Asked Questions:
Q: Can descriptive data mining be used to make predictions?
A: No, descriptive data mining is focused on describing and summarizing the data, and does not involve making predictions about future events.
Q: Can predictive data mining be used to describe the data?
A: Yes, predictive data mining involves analyzing the data to identify patterns and relationships that can be used to make predictions, which can also provide insights into the data.
Q: What are some examples of applications that use predictive data mining?
A: Some examples of applications that use predictive data mining include credit scoring, insurance