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Data mining can be defined as the process of extracting meaningful information and identifying specific patterns, relationships and anomalies in huge volumes of data, also referred to as data sets.
This can be achieved using specialized techniques and tools and information thus extracted an be utilized for a wide range of business purposes including increasing profits, cut costs, reduce risks, improve customer relationships and a lot more.
Data mining is also known as knowledge discovery in databases. Data scientists have long been interested in analyzing huge chunks of data to understand existing relationships as well as predict future trends. The term 'data mining' is a fairly newer term which came into existence as recently as the 1900s.
Data mining refers to all the processes and tools that are used to extract relevant and meaningful data from huge data sets. The mere accumulation of data does not provide us with meaningful information-it should be extracted from the unsorted data using sophisticated tools and techniques. Data mining allows governments and businesses to take informed decisions, based on specific patterns and trends. Data mining allows companies to provide better products and services to their customers, thereby improving customer experience and eventually, their own revenue.
Data mining is a domain that is made up of three important disciplines. They are Statistics, Artificial Intelligence and Machine Learning. Our experts provide specialized statistics assignment help as well as help with artificial intelligence and machine learning. Get in touch with us today for more information. While statistics inspects at the numerical aspects of data mining, artificial intelligence is more concerned with getting computers and machines to get closer to human intelligence. Machine learning is concerned with getting computer programs and software to 'learn' or 'teach itself' from specific trends that exists in the data set provided and make accurate predictions. Machine learning is a continuous process.
Data mining requires the deployment and use of sophisticated tools, software and equipment in order to get dependable and accurate results and predictions.
Given below are some important tools and applications that are used in data mining
RapidMiner is an important tool used in data mining. Considered one of the top tools for data analysis, it is written in Java programming Language and owned by RapidMiner. RapidMiner has inbuilt capacities for data preparation, machine learning, predictive analysis and deep learning. The best part, it comes in free and a number of paid versions. If you need to analyze upto 10000 rows of data, you can go for the free version. Else, opt for one of the paid versions. If you are a student, researcher or professor, you can request for a free license.
SAS stands for Statistical Analysis System and is owned by the SAS Institute. It is a software suite designed for advanced analytics, multivariate analysis, predictive analytics, data management and business intelligence. The software itself is written in C language and was developed at the North Carolina State University. SAS was incorporated as a company in 1976.
R, also referred to as the 'The R Project for Statistical Computing' is a free software programming language which also provides an environment for statistical analysis and data mining. R is written in C, Fortran and some parts of it is written in R itself. R comes with command line as well as graphical interfaces. The functionality and capability of R can be easily extended with a number of packages that are widely available. Click here for R programming assignment help
IBM SPSS is another great tool for data mining. It is currently owned by IBM and hence the name IBM SPSS. It comes with a graphical interface and needs no programming to work on it. According to IBM, it allows you to 'visualize the data mining process' and access both structured and non-structured data. Click here for IBM SPSS assignment help.
ORACLE DATA MINING is a data mining tool developed by Oracle. It comes with a graphical user interface that looks like an advanced version of Oracle SQL Developer. Oracle data mining is a part of Oracle Advance Analytics and comes with a number of data mining features for predictive analysis, data management and data management.
Apache Mahout is a part of the Apache Software Foundation. Apache Mahout is defined as a library of scalable machine learning algorithms and runs on top of Apache Hadoop. Apache Mahout provides the algorithms needed for data mining. Mahout uses the Map/Reduce paradigm for clustering, classification and filtering of data.
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