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

Machine learning is an application of Artificial Intelligence that supports an architecture with the capability to learn and enhance from experience without being definitely programmed automatically.It can be used by search engines including Google and Bing to rank internet pages or to determine which advertisement to display to which user. ... Read More

There are various applications of machine learning which are as follows −Social media services − Machine learning is an essential role in personalizing news feed to superior advertisement focusing over social media. Facebook needs machine learning to display news feed to the user based on its interests by treating items ... Read More

It is a statistical approach for preprocessing data to filter out irrelevant attributes or rank the relevant attribute. Measures of attribute relevance analysis can be used to recognize irrelevant attributes that can be unauthorized from the concept description process. The incorporation of this preprocessing step into class characterization or comparison ... Read More