# Data Mining Terminologies and Predictive Analytics Terms

## 1. Data Mining Terms – Objective

In this **Data Mining Tutorial**, we will study Data Mining Terminologies. We will cover each and every Data Mining Terminologies related to every domain. Moreover, we will discuss some predictive analytics terms used in Data Mining.

So, let’s start Data Mining Terminologies.

## 2. Data Mining Terminologies

Let’s begin data minin terminologies:

**i. Data Mining**

- Market Analysis

- Fraud Detection

- Customer Retention

- Production Control

- Science Exploration

**ii. Data Mining Engine**

- Characterization

- Association and Correlation Analysis

- Classification

- Prediction

- Cluster analysis

- Outlier analysis

- Evolution analysis

**iii. Knowledge Base**

- Knowledge Discovery

- Cleaning of data

- Data Integration

- Selection of data

- Transformation of data

- Data Mining

- Pattern Evaluation

- Knowledge Presentation

**iv. User Interface**

- Interact with the system by specifying a data mining query task.

- Providing information to help focus the search.

- Mining based on the intermediate data mining results.

- Browse database and data warehouse schemas or data structures.

- Evaluate mined patterns.

- Visualize the patterns in different forms.

**v. Data Integration**

**vi. Associations**

**vii. Backpropagation**

**viii. Binning**

**ix. CART**

**x. Categorical data**

**xi. CHAID**

**xii. Chi-squared**

**xiii. Classification**

**xiv. Classification tree**

**xv. Cleaning (cleansing)**

**xvi. Confusion matrix**

**xvii. Consequent**

**xviii. Continuous**

**xix. Cross-validation**

**xx. Data**

**xxi. DBMS**

**xxii. Data format**

**xxiii. Decision Tree**

**xxiv. Data Mining method**

**xxv. Deduction**

**xxvi. Degree of fit**

**xxvii. Dependent Variable**

**xxviii. Deployment**

**xxixDimension**

**xxx. Discrete**

**xxxi.Discriminant analysis**

**xxxii. Entropy**

**xxxiii. Exploratory Analysis**

**xxxiv. External Data**

**xxxv.Feed-forward**

**xxxvi. Fuzzy Logic**

**xxxvii. Genetic Algorithms**

**xxxviii. GUI**

**xxxix. Independent variable**

**xl. Induction**

**xli. Interaction**

**xlii. Internal data**

**xliii. k-nearest neighbor**

**xliv. Kohonen Feature Map**

**xlv. Layer**

**xlvi. Leaf**

**xlvii. Learning**

**xlviii. Least Squares**

**xlix. MARS**

**l. Maximum likelihood**

**li. Mean**

**lii. Median**

**liii. Missing data**

**liv. Mode**

**lv. Node**

**lvi. Noise**

**lvii. Non-applicable Data**

**lviii. Normalize**

**lix. OLAP**

**lx. Optimization Criterion**

**lxi. Outliers**

**lxii. Overfitting**

**lxiii. Overlay**

**lxiv. Parallel processing**

**lxv. Prevalence**

**lxvi. Pruning**

**lxvii. Range**

**lxviii. RDBMS**

**lxix. Regression Tree**

**lxx. Resubstitution Error**

**lxxi. Right-hand side**

**lxxii. R-squared**

**lxxiii. Sampling**

**lxxiv. Sensitivity Analysis**

**lxxv. Sequence Discovery**

**lxxvi. SMP**

**lxxvii. Standardize**

**lxxviii. Support**

**lxxix. Test data**

**lxxx. Test Error**

**lxxxi. Time Series**

**lxxxii. Time Series Model**

**lxxxiii. Topology**

**lxxxiv. Training**

**lxxxv. Training data**

**lxxxvi. Transformation**

**lxxxvii. Unsupervised Learning**

**lxxxviii. Validation**

**lxxxix. Variance**

**xc. Visualization**

**xci. Windowing**

## 3. Conclusion

As a result, we have studied Data Mining Terminologies. As these terminologies for data mining will help you to understand each and every small concept related to data mining. Furthermore, if you feel any query feel free to ask in a comment section.

Related Topic – **Clustering In Data Mining**