描述性和预测性数据挖掘之间的区别
描述性挖掘：
该术语主要用于产生相关性、交叉表、频率等。这些技术用于确定数据中的相似性并找到现有模式。描述性分析的另一种应用是在可用数据的主要部分中开发引人入胜的亚组。
这种分析强调将数据汇总和转换为用于报告和监控的有意义的信息。
预测数据挖掘：
这种挖掘的主要目标是谈论未来的结果，而不是当前的行为。它使用监督学习函数来预测目标值。属于此类挖掘类别的方法称为分类、时间序列分析和回归。数据建模是预测分析的必要条件，它通过利用当前的一些变量来预测其他变量的未来未知数据值。
描述性和预测性数据挖掘之间的区别：
| S.No. | Comparison | Descriptive Data Mining | Predictive Data Mining |
|---|---|---|---|
| 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. |
| 2. | Preciseness | It provides accurate data. | It produces results does not ensure accuracy. |
| 3. | Practical analysis methods | Standard reporting, query/drill down and ad-hoc reporting. | Predictive modelling, forecasting, simulation and alerts. |
| 4. | Require | It requires data aggregation and data mining | It requires statistics and forecasting methods |
| 5. | Type of approach | Reactive approach | Proactive approach |
| 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. |
| 7. | Methods(in general) |
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