Data Mining Definition
Nowadays, every now and then, we hear the word “Data Mining”. Let’s know the Data Mining Definition and why it is so popular nowadays in technical industries and elsewhere.
Data Mining Definition:
Data Mining Definition says it is a process of discovering/finding patterns and hidden relationships within the large data sets. It is used to analyze the data sets and extract usable or relevant information from them.
Data mining has application in many fields such as research, science, business and government security to detect the frauds and also for analyzing the trends of insurance, banking, retail, etc.
Data mining uses the techniques from artificial intelligence, neural networks (like backpropagation algorithm), statistics and machine learning with database management to analyze the large volume of data sets. Data mining is synonyms for knowledge discovery in databases (KDD).
KDD is an iterative sequence of following:
- Data Cleaning: It is a process of removing noise, inconsistent, unrelated data and also to detect and remove outliers from the database.
- Data Integration: It is a process of combining two or more than two or multiple data.
- Data Selection: It a process of selecting relevant data from the database.
- Data Transformation: It is a process of transforming the data from one form to other form or consolidating the data.
- Data Mining: It is an essential process where the intelligent method is applied to extract the data patterns.
- Pattern Evaluation: It is a process of identifying the truly interesting patterns.
- Knowledge Presentation: It is a process of visualization and this technique is used to present mined knowledge to the users.
According to data mining definition, it allows many tools to analyze the large volume of data collections, finding patterns and hidden relationships.
Several techniques are used in Data mining as we know from Data Mining Definition for analyzing and predicting the future outcomes. Some of them are as follows:
- Clustering: Clustering is a process of grouping similar data values or data points in such a way that it makes a cluster.
- Classification: Classification is a technique to classify the data sets or object into categories or class based on its characteristics and features.
- Prediction: Prediction is a process of identifying data points or future outcome based on previous data value or data sets.
- Association: Association is a process of discovering the probability of frequent patterns, associations, and correlations from data sets. Association rules are if and then statement. The if– part is called antecedents and then-part is called consequents. It is created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. It is very useful for analyzing and predicting the customer’s behaviour.
- Decision Tree: Decision tree builds classification or regression models in the form of a tree structure. The root of decision tree acts as a condition. Each answer leads to specific data that help us to determine final decision based upon it.