Found 422 Articles for Data Mining

The most basic difference between classification and clustering is that classification is used with supervised learning technique, whereas clustering is used with unsupervised learning technique. In classification, the computer is given a label to use in classifying new observations. For the label verification in this case, the machine requires thorough testing and training. Classification is therefore a more intricate procedure than clustering. In contrast, clustering is an unsupervised learning method that groups data based on similarities. Here, there is no need for training because the machine learns from the already−existing data. In this article, we will discuss the important ... Read More

Spatial Data MiningSpatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial records to create business intelligence or multiple results. This needed specific techniques and resources to obtain the geographical information into relevant and useful formats.The evolution of spatial data and extensive usage of spatial databases has governed spatial knowledge discovery. Spatial data mining can be learned as a process that decides some astonishing and hypothetically valuable patterns from spatial databases.There are several challenges involved in spatial data mining include recognizing patterns or discovering objects that are relevant to ... Read More

Descriptive Data MiningDescriptive mining is generally used to provide correlation, cross-tabulation, frequency, etc. These methods are used to decide the data's regularities and to reveal patterns. It focuses on the summarization and conversion of records into significant data for reporting and monitoring.Descriptive mining "describes" the data. Once the data is captured, it can modify it into human interpretable form. In descriptive data mining, an association technique that uses Apriori algorithms to characterize student performance to find co-relations between a set of items.The Apriori algorithm is used in the database including academic records of several students and tries to extract association ... Read More

Business Intelligence is a collection of procedures, mechanisms, and technologies that modifies raw data into significant information that drives cost-effective business services. It is a collection of software and services to modify data into actionable intelligence and recognition. BI has an unambiguous impact on an organization’s methods, tactical and operational business decisions. BI supports fact-based decision-making using historical data rather than assumptions and gut feelings.BI tools implement data analysis and make documents, summaries, dashboards, maps, graphs, and charts to support users with detailed intelligence about the feature of the business.Business Intelligence is one of the most dynamic tools many organizations ... Read More

ETLETL 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.It is tempting to think a creating a Data warehouse is simply extracting data from multiple sources and loading it into the database of a Data warehouse. The ETL process needed active inputs from multiple stakeholders including developers, analysts, testers, top administration, and is technically difficult.It can support its value as a tool for decision-makers. Data warehouse system needs to change with business development. ETL is a constant activity ... Read More

ELT stands for Extract, Load, and Transform. It is a data integration process for transferring raw data from a source server to a data system (such as a data warehouse or data lake) on a target server and then fitting the data for downstream uses.The extract and load procedure can be isolated from the transformation phase. Isolating the load phase from the transformation process deletes an inherent dependency between these phases. It can include the data necessary for the transformations, the extract and load process can include an element of data that can be essential in the future. The load ... Read More

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