ETL stands for Extract, transform, and load. It is the process data-driven organizations use to gather data from multiple sources and then bring it together to support discovery, reporting, analysis, and decision-making.The data sources can be divergent in type, format, volume, and reliability, hence the data required to be processed ... Read More

Data mining is the phase of discovering useful new correlations, patterns, and trends by transferring through a high amount of records saved in repositories, using pattern recognition technologies including statistical and numerical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in ... Read More

Association rule learning is a kind of unsupervised learning technique that tests for the reliance of one data element on another data element and design appropriately so that it can be more cost-effective. It tries to discover some interesting relations or associations between the variables of the dataset. It depends ... Read More

Statistics is the science of learning from data. It contains everything from planning for the set of records and subsequent data administration to end-of-the-line activities including drawing inferences from numerical facts called data and presentation of results. Statistics is concerned with the most essential of person required: the need to ... Read More

Model-based clustering is a statistical approach to data clustering. The observed (multivariate) data is considered to have been created from a finite combination of component models. Each component model is a probability distribution, generally a parametric multivariate distribution.For instance, in a multivariate Gaussian mixture model, each component is a multivariate ... Read More

The grid-based clustering methods use a multi-resolution grid data structure. It quantizes the object areas into a finite number of cells that form a grid structure on which all of the operations for clustering are implemented. The benefit of the method is its quick processing time, which is generally independent ... Read More

There are two types of partitional algorithms which are as follows −K-means clustering − K-means clustering is the most common partitioning algorithm. K-means reassigns each data in the dataset to only one of the new clusters formed. A record or data point is assigned to the nearest cluster using a ... Read More

Relational database systems supports five built-in aggregate functions such as count(), sum(), avg(), max() and min(). These aggregate functions can be used as basic measures in the descriptive mining of multidimensional information. There are two descriptive statistical measures such as measures of central tendency and measures of data dispersion can ... Read More

Unsupervised learning is when it can provide a set of unlabelled data, which it is required to analyze and find patterns inside. The examples are dimension reduction and clustering. The training is supported to the machine with the group of data that has not been labeled, classified, or categorized, and ... Read More

Some persons are utilizing machine learning in their normal life. Consider that it is engaging with the web, defining our preferences, likes, and dislikes through our searches. Some things are chosen up by cookies appearing on our device; from this, the behavior of a customer is computed. It supports to ... Read More