Definitions:

• also known as Knowledge Discovery in Databases (KDD)
• refers to the unsupervised learning of patterns/rules/correlations from data
• is about finding anomalies, patterns, and correlations within large data sets to predict outcomes
• is a field where we try to identify patterns in data and come up with initial insights.
• uses cluster analysis, anomaly detection, association rule mining etc. to find out patterns in large datasets.
• uses power of machine learning, statistics and database techniques to mine large databases and come up with patterns.
• is finding out hidden and interesting patterns stored in large data warehouses using the power of statistics, artificial intelligence, machine learning and database management techniques.
• Explorative – Dig out the data first, discover novel patterns and then make theories.
• Involves Data Cleaning
• Usually involves working with large datasets.
• Makes generous use of heuristics think
• Inductive process (involves making predictions)
• Numeric and Non-Numeric Data
• Less concerned about data collection.
• Some of the popular data mining methods include Estimation, Classification, Neural Networks, Clustering, Association, and Visualization.

References:

• https://www.dezyre.com/article/data-mining-vs-statistics-vs-machine-learning/349