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 to be helpful when delivered together. The target data stores can be databases, data warehouses, or data lakes, based on the objectives and technical execution. There are the following steps of ETL which are as follows −Extract − During extraction, ETL recognizes the data and duplicate it from its sources, ... 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 novel methods that are both logical and helpful to the data owner.It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.Data Mining is similar ... 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 on various rules to find interesting relations between variables in the database.The association rule learning is the most important approach of machine learning, and it is employed in Market Basket analysis, Web usage mining, continuous production, etc. In market basket analysis, it is an approach used by several big retailers ... 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 discover out more about the globe and how it works in face of innovation and uncertainty.Information is the communication of knowledge. Data are referred to be crude data and not knowledge by themselves. The sequence from data to knowledge is as follows: from data to information (data develop into information ... 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 Gaussian distribution. The component responsible for generating a particular observation determines the cluster to which the observation belongs.Model-based clustering is a try to advance the fit between the given data and some mathematical model and is based on the assumption that data are created by a combination of a basic ... 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 of the number of data objects, still dependent on only the multiple cells in each dimension in the quantized space.The grid-based clustering uses a multi-resolution grid data structure and uses dense grid cells to form clusters. There are several interesting methods are STING, wave cluster, and CLIQUE.STING − A statistical ... 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 the algorithm required to facilitate on that data without some supervision. The objective of unsupervised learning is to restructure the input record into new features or a set of objects with same patterns.Cluster analysis is used to form groups or clusters of the same records depending on various measures made ... 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 measure of distance or similarity. There are the following steps used in the K-means clustering:It can select K initial cluster centroid c1, c2, c3 ... . ck.It can assign each instance x in the S cluster whose centroid is nearest to x.For each cluster, recompute its centroid based on which ... 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 be used effectively in high multidimensional databases.Measures of central tendency − Measures of central tendency such as mean, median, mode, and mid-range.Mean − The arithmetic average is evaluated simply by inserting together all values and splitting them by the number of values. It uses data from every single value. Let ... 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 is defined as an analytical characterization.Data discrimination makes discrimination rules which are a comparison of the general features of objects between two classes defined as the target class and the contrasting class.It is a comparison of the general characteristics of targeting class data objects with the general characteristics of objects ... Read More