Data Visualization is not a step in the KDD process, but rather a technique used to present the results of data mining.

The primary goal of Data Mining is to uncover hidden patterns and relationships in data that can be used to make informed decisions.

K-Nearest Neighbors (KNN), Decision Trees, and Support Vector Machines (SVM) are all widely used Data Mining algorithms for classification tasks.

Data Cleaning is the process of identifying and removing irrelevant or duplicate data from a dataset to improve its quality.

Regression is a statistical technique used to predict continuous values, while Classification, Clustering, and Association Rule Mining are all Data Mining tasks.

Data Transformation is the process of converting data into a format that is suitable for Data Mining, such as normalizing numeric values or converting categorical values to numerical values.

K-Means Clustering, Hierarchical Clustering, and Density-Based Clustering are all widely used Data Mining algorithms for clustering tasks.

Data Integration is the process of combining data from multiple sources into a single, cohesive dataset, often involving resolving data conflicts and ensuring data consistency.

Apriori Algorithm, FP-Growth Algorithm, and Eclat Algorithm are all widely used Data Mining algorithms for Association Rule Mining.

Data Reduction is the process of reducing the size of a dataset while preserving its essential information, often involving techniques such as sampling, dimensionality reduction, and feature selection.

Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machines (OCSVM) are all widely used Data Mining algorithms for anomaly detection.

Data Mining is the process of discovering hidden patterns and relationships in data, often using statistical and machine learning techniques.

Dimensionality Reduction is a technique used to reduce the number of features in a dataset, while preserving its essential information, and is not a type of Data Mining task.

Data Visualization is the process of presenting the results of Data Mining in a visual format, such as charts, graphs, and maps, to make them easier to understand and interpret.

Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Linear Discriminant Analysis (LDA) are all widely used Data Mining algorithms for dimensionality reduction.