## Challenges of Data Mining

Nowadays Data Mining and knowledge discovery are evolving a crucial technology for business and researchers in many domains.Data Mining is developing into established and trusted… Read More »

- Data Mining | Set 2
- Difference between a Data Analyst and a Data Scientist
- Data Normalization in Data Mining
- Relationship between Data Mining and Machine Learning
- Data Integration in Data Mining
- Numerosity Reduction in Data Mining
- r-Nearest neighbors
- Redundancy and Correlation in Data Mining
- Web Mining
- ML | Log Loss and Mean Squared Error

Nowadays Data Mining and knowledge discovery are evolving a crucial technology for business and researchers in many domains.Data Mining is developing into established and trusted… Read More »

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 »