what are the tools for data mining?
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['Rapid Miner. ... ', 'Oracle Data Mining. ... ', 'IBM SPSS Modeler. ... ', 'KNIME. ... ', 'Python. ... ', 'Orange. ... ', 'Kaggle. ... ', 'Rattle.']
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['Statistics, Calculus, Linear Algebra and Probability. A) Statistics contain tools that are used to get an outcome from data. ... ', 'Programming Knowledge. Being able to write code is one of the most important things when it comes to Machine Learning. ... ', 'Data Modeling.']
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[' Hadoop: The Apache Hadoop software library is a big data framework. ... ', ' HPCC: HPCC is a big data tool developed by LexisNexis Risk Solution. ... ', ' Storm: Storm is a free and open source big data computation system. ... ', ' Qubole: ... ', ' Cassandra: ... ', ' Statwing: ... ', ' CouchDB: ... ', ' Pentaho:']
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Text mining. ... Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics.
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Data mining is the process of determining data patterns. A data warehouse is a database system designed for analytics. Data mining is generally considered as the process of extracting useful data from a large set of data. Data warehousing is the process of combining all the relevant data.
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In conclusion, Python seems to be the most widely used programming language for data scientists today. This language allows the integration of SQL, TensorFlow, and many other useful functions and libraries for data science and machine learning.
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['Analytica — visual analytics and statistics package.', 'Angoss — products KnowledgeSEEKER and KnowledgeSTUDIO incorporate several data mining algorithms.', 'ASReml — for restricted maximum likelihood analyses.', 'BMDP — general statistics package.', 'Cornerstone — engineering & DoE focused statistical software.']
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['Application scripting.', 'Array programming.', 'Artificial intelligence reasoning.', 'Cloud computing.', 'Computational statistics.', 'Contact Management Software.', 'E-commerce.', 'Financial time series analysis.']
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Data warehouse refers to the process of compiling and organizing data into one common database, whereas data mining refers to the process of extracting useful data from the databases. The data mining process depends on the data compiled in the data warehousing phase to recognize meaningful patterns.
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['SAS. It is one of those data science tools which are specifically designed for statistical operations. ... ', 'Apache Spark. ... ', 'BigML. ... ', 'D3. ... ', 'MATLAB. ... ', 'Excel. ... ', 'ggplot2. ... ', 'Tableau.']
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['Flowchart. Most of us are familiar with flowcharts. ... ', 'Check Sheet. A check sheet is a basic quality tool that is used to collect data. ... ', 'Cause and Effect (fish bone) Diagram. ... ', 'Pareto Chart. ... ', 'Control Charts. ... ', 'Histograms. ... ', 'Scatter Diagrams.']
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['Step 1: Understand the Business. ... ', 'Step 2: Get Your Data. ... ', 'Step 3: Explore and Clean Your Data. ... ', 'Step 4: Enrich Your Dataset. ... ', 'Step 5: Build Helpful Visualizations. ... ', 'Step 6: Get Predictive. ... ', 'Step 7: Iterate, Iterate, Iterate.']
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Big data analysis caters to a large amount of data set which is also known as data mining, but data science makes use of the machine learning algorithms to design and develop statistical models to generate knowledge from the pile of big data.
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Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. Data mining tasks can be classified into two categories: descriptive and predictive. ... Predictive mining tasks perform inference on the current data in order to make predictions.
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Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
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How Machine Learning Works. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
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Data mining is more about digging data, discovering patterns and coming up with theories to get to inferences. But the methods of statistical analysis can be applied only on data that is cleansed. Statistics is more about confirmation and applying the various theories.