## Relationship between Data Mining and Machine Learning

There is no universal agreement on what “Data Mining” suggests that. The focus on the prediction of data is not always right with machine learning,… Read More »

- Data Preprocessing in Data Mining
- Data-Mining | Fact Constellation in Data Warehouse modelling
- Difference between a Data Analyst and a Data Scientist
- r-Nearest neighbors
- ML | Log Loss and Mean Squared Error
- Attribute Subset Selection in Data Mining
- ML | R-squared in Regression Analysis
- Data Mining | Set 2
- Numerosity Reduction in Data Mining
- Web Mining
- Relationship between Data Mining and Machine Learning
- Data Normalization in Data Mining
- Data Integration in Data Mining
- Redundancy and Correlation in Data Mining

There is no universal agreement on what “Data Mining” suggests that. The focus on the prediction of data is not always right with machine learning,… Read More »

Data Mining may be a term from applied science. Typically it’s additionally referred to as data discovery in databases (KDD). Data processing is concerning finding… Read More »

Data Analysts analyze similar historical knowledge to realize info. Information |the data} generated will not be used more to boost the understanding of the system.… Read More »

Data Integration is a data preprocessing technique that involves combining data from multiple heterogeneous data sources into a coherent data store and provide a unified… Read More »

Web Mining is the process of Data Mining techniques to automatically discover and extract information from Web documents and services. The main purpose of web… Read More »

Log Loss It is the evaluation measure to check the performance of the classification model. It measures the amount of divergence of predicted probability with… Read More »

r-Nearest neighbors is a modified version of the k-nearest neighbors. The issue with k-nearest neighbors is the choice of k. A smaller k, the classifier… Read More »

Normalization is used to scale the data of an attribute so that it falls in a smaller range, such as -1.0 to 1.0 or 0.0… Read More »

Prerequisites:Chi-square test, covariance-and-correlation What is Data Redundancy ? During data integration in data mining, various data stores are used. This can lead to the problem… Read More »

Prerequisite: Data preprocessing Why Data Reduction ? Data reduction process reduces the size of data and makes it suitable and feasible for analysis. In the… Read More »

R-squared is a statistical measure that represents the goodness of fit of a regression model. The ideal value for r-square is 1. The closer the… Read More »

Attribute subset Selection is a technique which is used for data reduction in data mining process. Data reduction reduces the size of data so that… Read More »

Preprocessing in Data Mining: Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format.… Read More »

Fact Constellation is a schema for representing multidimensional model. It is a collection of multiple fact tables having some common dimension tables. It can be… Read More »

A data warehouse is built to support management functions whereas data mining is used to extract useful information and patterns from data. Data warehousing is… Read More »