Difference Between Descriptive and Predictive Data Mining

The descriptive and predictive data mining techniques are used in data mining to mine the types of patterns. The descriptive analysis is used to mine data and provide the latest information on past or recent events. On the other hand, the predictive analysis provides answers of the future queries that move across using historical data as the chief principle for decisions.

Data mining tasks can be descriptive, predictive and prescriptive. Here we are just discussing the two of them descriptive and prescriptive. In simple words, descriptive implicates discovering the interesting patterns or association relating the data whereas predictive involves the prediction and classification of the behaviour of the model founded on the current and past data.

Content: Descriptive Data Mining Vs Predictive Data Mining

1. 1. Comparison Chart
   2. Definition
   3. Key Differences
   4. Conclusion

Comparison Chart

Basis for comparison	Descriptive Mining	Predictive Mining
Basic	It identifies, what happened in the past by analyzing stored data	It describes, what can happen in the future with the help past data analysis.
Require	Data aggregation and data mining	Statistics and forecasting methods
Preciseness	Provides accurate data	Produces results does not ensure accuracy.
Type of approach	Reactive	Proactive
Practical analysis methods	Standard reporting, query/drill down and ad-hoc reporting.	Predictive modelling, forecasting, simulation and alerts.

Definition of Descriptive Data Mining

Descriptive mining is generally used to produce correlation, cross tabulation, frequency etcetera. These techniques are determined to find the regularities in the data and to reveal patterns. The other application of descriptive analysis is to discover the captivating subgroups in the major part of the data.

Descriptive analytics focuses on the summarization and conversion of the data into meaningful information for reporting and monitoring. Furthermore, it permits to examine the data in a detailed way so that it would be able to answer easily about “what has happened?” and “what is happening?”. Clustering, summarization, association are the techniques categorized under descriptive mining.

Definition of Predictive Data Mining

The primary objective of predictive mining is to predict future results instead of current behaviour. It involves the supervised learning functions used for the prediction of the target value. The methods fall under this mining category are the classification, time-series analysis and regression. Data modelling is the necessity of the predictive analysis, which works by utilizing some variables to anticipate the unknown future data values for other variables.

Additionally, it also conducts the comparison among these supervised learning methods for obtaining the prescience about the strength and weaknesses of each approach. This whole process is performed to find out the most suitable method for extracting the desired knowledge. The predictive analysis is used for providing information about “what might happen?” and “why it might happen?”.

Conclusion

Descriptive mining employs unsupervised learning functions while predictive uses supervised learning techniques. This is the reason the descriptive analysis is not able to anticipate the unknown target values but concentrates more on the intrinsic arrangement, interconnections and relations. Conversely, predictive mining specifies and distinguishes a set of data for future prediction.