DATA data. In other words, it identifies useful

      DATA MINING           Dexter GoseCSE2DBX– Database FundamentalsAssessment3December10, 2017    Descriptionof Data miningData mining,also referred to as Knowledge Discovery from Data (KDD), denotes a process usedto extract knowledge or patterns from an array of data. These materials aresourced from data warehouses, databases, the Internet, and other repositories.Generally,data mining is applicable to almost every type of data provided that they aredeemed significant for a specific application. These data forms utilized formining applications are not only limited to database, data warehouse, andtransactions, but also spatial, text, sequence, multimedia, data streams, graphor networked data, as well as the World Wide Web.

Data mining uses complex mathematicalalgorithms to categorize data and determine the likelihood of forthcomingevents. This whole process involves a number ofstages which commences from the extraction and management of data up toanalysis, definition, and graph representation. Initially, the data areextracted, transformed, and loaded as transaction data onto the data warehouse.

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Once this is completed, they are stored and managed in a multi-dimensionaldatabase system.  Analysts andspecialists are then provided access to these data which are represented in a suitablelayout such as in the form of a table or a graph.  TheImportance of Data miningIn this modern age, the importance of datamining cannot be denied due to its various benefits. Data mining applies softwareprograms to categorize various patterns and relationships in a series of data.

In other words, it identifies useful pieces of data which are relevant to anumber of areas. These extracted information enable businesses to come up with knowledge-drivenresults for numerous aspects such as advertising, marketing,and the introduction of new of products andservices, among others. Owing to the application of these data miningtools, businesses can now easily provide resolutions to queries which werepreviously considered as time-consuming.Data mining is also of great importance in data pre-processing,data cleaning, as well as database integration. For instance, researchers cannow easily determine similar data patterns, identify co-existing sequences, asidefrom finding out associations between a series of research activities. Consequently,these bring groundbreakingchanges to the field of research, as data mining presents more comprehensibledata forms.

Data miningImplementation and Software   Data miningin IndustryThe process of datamining is primarily utilizedthese days by a number of industries including those related to marketing,retail, finance, and communication, among other fields.One of theseindustries is healthcare or medical science in which the future direction of ahealth care system is analyzed through data mining tools. Healthcareproviders make use of data mining to determine the most efficient treatmentsand practices by making a comparison of several causes, symptoms, treatmentsand effects prior to analysing which action will demonstrate the utmosteffectiveness for the patients. Data mining also plays a pivotal role in numerousareas of manufacturing through the discovery of useful patterns in complexmanufacturing processes with the use of data mining tools. It is also utilizedto establish control parameters that allow optimum production to be accuratelyreproduced from one factory to another, while also enabling manufacturers todetect any faulty equipment during the process.Furthermore, data mining has also proven itssignificance in the field of finance or banking through the use of customerdata in order to render better credit decisions, as well as to identifypossibly fraudulent activities. For example, a financial institution can assessboth good and bad loans, and protect credit card owners when transactions aremade.Likewise, it also provides an identical roleto the government by looking into citizens’ financial records which can be appliedto determine potential criminal activities like money laundering.

The large number of crime datasets, together with the intricacyof relations between these data forms have led to criminology as a suitablefield to make use of the different techniques of data mining.                            



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