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In the vast data ocean, discovering and using the valuable information has become the key technology.
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- Handbook of Statistical Analysis and Data Mining Applications
- Information-Statistical Data Mining
- Data Mining & Machine Learning
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Handbook of Statistical Analysis and Data Mining Applications
Data analysis is a process of inspecting, cleansing , transforming , and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.
Journal Citation Reports (Clarivate Analytics): / (Computer Science, Online ISSN Statistical Analysis and Data Mining addresses the broad area of data Abstract · Full text · PDF · References · Request permissions A framework for stability‐based module detection in correlation graphs.
Information-Statistical Data Mining
Statistical Analysis software allows organizations to take full advantage of the data they possess to uncover business opportunities and increase revenue. Capterra is free for users because vendors pay us when they receive web traffic and sales opportunities. Capterra directories list all vendors—not just those that pay us—so that you can make the best-informed purchase decision possible.
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Data Mining refers to a process by which patterns are extracted from data. Such patterns often provide insights into relationships that can be used to improve business decision making. Statistical data mining tools and techniques can be roughly grouped according to their use for clustering, classification, association, and prediction. Clustering refers to data mining tools and techniques by which a set of cases are placed into natural groupings based upon their measured characteristics. Since the number of characteristics is often large, a multivariate measure of similarity between cases needs to be employed.
Data Mining & Machine Learning
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The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers both academic and industrial through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Business analysts, scientists, engineers, researchers, and students in statistics and data mining. Theory Chapter 3.
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