Data mining
Resource Information
The concept Data mining represents the subject, aboutness, idea or notion of resources found in University of Missouri Libraries.
The Resource
Data mining
Resource Information
The concept Data mining represents the subject, aboutness, idea or notion of resources found in University of Missouri Libraries.
- Label
- Data mining
A sample of Items that share the Concept Data mining See All
Context
Context of Data miningSubject of
No resources found
No enriched resources found
- 2015 IEEE 1st International Workshop on Software Analytics (SWAN)
- A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data
- A data mining study of g-quadruplexes and their effect on DNA replication
- A data mining study of ranking within social networks
- A graph analytics framework for knowledge discovery
- A guide to improving data integrity and adoption : a case study in verifying usage data
- A new adaptive framework for collaborative filtering prediction
- A practical guide to data mining for business and industry
- A whirlwind tour of Python
- AI and analytics in production : how to make it work
- Accelerating discovery : mining unstructured information for hypothesis generation
- Actionable web analytics : using data to make smart business decisions
- Active mining : new directions of data mining
- Adaptive stream mining : pattern learning and mining from evolving data streams
- Adaptive web sites : a knowledge extraction from web data approach
- Advanced Jupyter Notebook development : deployment and customization of notebooks, tmpnb, nbviewer and JupyterHub
- Advanced agent technology : AAMAS 2011 Workshops, AMPLE, AOSE, ARMS, DOCM3AS, ITMAS, Taipei, Taiwan, May 2-6, 2011. Revised selected papers
- Advanced analytics and real-time data processing in Apache Spark
- Advanced data analytics using Python : with machine learning, deep learning and NLP examples
- Advanced data mining and applications : 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, proceedings, Part I
- Advanced data mining and applications : 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, proceedings, Part II
- Advanced data mining technologies in bioinformatics
- Advanced methods for knowledge discovery from complex data
- Advanced statistics and data mining for data science
- Advanced techniques for knowledge engineering and innovative applications : 16th International Conference, KES 2012, San Sebastian, Spain, September 10-12, 2012, revised selected papers
- Advanced techniques in knowledge discovery and data mining
- Advances in Computational Algorithms and Data Analysis
- Advances in K-means clustering : a data mining thinking
- Advances in data mining : applications in E-commerce, medicine, and knowledge management
- Advances in data mining : applications in E-commerce, medicine, and knowledge management
- Advances in digital forensics VII : 7th IFIP WG 11.9 International Conference on Digital Forensics, Orlando, FL, USA, January 31 -February 2, 2011 : revised selected papers
- Advances in intelligent data analysis X : 10th international symposium, IDA 2011, Porto, Portugal, October 29-31, 2011 : proceedings
- Advances in mass data analysis of images and signals in medicine, biotechnology, chemistry, and food industry : Third International Conference, MDA 2008 Leipzig, Germany, July 14, 2008 : proceedings
- Advances in neural networks-- ISNN 2014 : 11th International Symposium on Neural Networks, ISNN 2014, Hong Kong and Macao, China, November 28- December 1, 2014 : proceedings
- Advances in nonlinear speech processing : 5th International Conference on Nonlinear Speech Processing, NOLISP 2011, Las Palmas de Gran Canaria, Spain, November 7-9, 2011, proceedings
- Advances in social media analysis
- Agent intelligence through data mining
- Agent intelligence through data mining
- Agents and data mining interaction : 7th international workshop, ADMI 2011, Taipei, Taiwan, May 2-6, 2011 : revised selected papers
- Agile data science
- Agile data science 2.0 : building full-stack data analytics applications with Spark
- An introduction to data science
- An introduction to support vector machines : and other kernel-based learning methods
- An introduction to text mining : research design, data collection, and analysis
- An overview of ARL's multimodal signatures database and web interface
- Analiza danych w bizenesie : Sztuka podejmowania skutecznych decyzji
- Analysis of rare categories
- Analysis of symbolic data : exploratory methods for extracting statistical information from complex data
- Analyzing big data with Hadoop, AWS, and EMR : understanding how to use Hadoop on Amazon's Elastic MapReduce Service
- Analyzing big data with Spark and Amazon EMR : learning to harness the power of cloud computing to analyze big data when you don't have a cluster of your own
- Analyzing data in the Internet of Things : a collection of talks from Strata + Hadoop World 2015
- Analyzing data using Spark 2.0 DataFrames with Python : learning the basics of Python and Spark DataFrames, and how they work with big data
- Apache Spark 2 data processing and real-time analytics : master complex big data processing, stream analytics, and machine learning with Apache
- Apache Spark 2.0
- Apache Spark for Data Science Cookbook
- Apache Spark for data science cookbook : overinsightful 90 recipes to get lightning-fast analytics with Apache Spark
- Apache Spark with Python : big data with PySpark and Spark
- Apache Spark with Scala
- Apache Spark with Scala : learn Spark from a big data guru
- Applications of data mining in computer security
- Applied Data Mining
- Applied data mining : statistical methods for business and industry
- Applied data mining for business analytics
- Applied data mining for business and industry
- Applied data mining for forecasting using SAS
- Applied unsupervised learning with R
- Applying text timing in corporate spin-off disclosure statement analysis: understanding the main concerns and recommendation of appropriate term weights
- Architectural considerations for Hadoop applications : using clickstream analytics as an end-to-end example
- Ask, measure, learn : using social media analytics to understand and influence customer behavior
- Ask, measure, learn : using social media analytics to understand and influence customer behavior
- Assessing and improving prediction and classification : theory and algorithms in C++
- Association rule mining : models and algorithms
- Association rule mining in cooperative research
- Automating the design of data mining algorithms : an evolutionary computation approach
- Automating the design of data mining algorithms : an evolutionary computation approach
- Automation of nuclear fuel pellet inspection using machine vision
- Automation of nuclear fuel pellet inspection using machine vision
- Avro data
- Azure masterclass : Docker containers in the cloud with AWS ECS
- Azure masterclass : analyze data with Azure Stream Analytics
- Azure masterclass : manage Azure cloud with ARM templates
- Badanie danych Raport z pierwszej linii działań
- Bankruption : how community banking can survive fintech
- Basic statistics and data mining for data science
- Become a Python data analyst
- Become a Python data analyst : perform exploratory data analysis and gain insight into scientific computing using Python
- Beginning data analysis with Python and Jupyter : use powerful industry-standard tools to unlock new, actionable insight from your existing data
- Beginning data analytics with RapidMiner : understanding core analytical methods and how to use them in RapidMiner
- Behind every good decision : how anyone can use business analytics to turn data into profitable insight
- Benchmark development for the evaluation of visualization for data mining
- Better intelligence from the new Google trends
- Big Data : Entwicklung und Programmierung von Systemen für große Datenmengen und Einsatz der Lambda-Architektur
- Big Data Analytics with Applications in Insider Threat Detection
- Big Data For Dummies
- Big Data analytics with R and Hadoop : set up an integrated infrastructure of R and Hadoop to turn your data analytics into Big Data analytics
- Big Data revolution : what farmers, doctors and insurance agents teach us about discovering Big Data patterns
- Big crisis data : social media in disasters and time-critical situations
- Big data : algorithms, analytics, and applications
- Big data : algorithms, analytics, and applications
- Big data : principles and best practices of scalable real-time data systems
- Big data : related technologies, challenges and future prospects
- Big data @ work : dispelling the myths, uncovering the opportunities
- Big data MBA : driving business strategies with data science
- Big data analysis : new algorithms for a new society
- Big data analytics
- Big data analytics : a practical guide for managers
- Big data analytics : turning big data into big money
- Big data analytics : turning big data into big money
- Big data analytics beyond Hadoop : real-time applications with Storm, Spark, and more Hadoop alternatives
- Big data analytics beyond hadoop : real-time applications with storm, spark, and more hadoop alternatives
- Big data analytics for intelligent healthcare management
- Big data analytics for intelligent healthcare management
- Big data analytics using Apache Spark
- Big data analytics using Splunk
- Big data analytics with Java : big data analytics - massive, predictive, social and self-driving
- Big data analytics with Spark : a practitioner's guide to using Spark for large-scale data processing, machine learning, and graph analytics, and high-velocity data stream processing
- Big data analytics with Spark : a practitioner's guide to using Spark for large-scale data processing, machine learning, and graph analytics, and high-velocity data stream processing
- Big data and business analytics
- Big data application architecture Q & A : a problem-solution approach
- Big data benchmarks, performance optimization, and emerging hardware : 4th and 5th Workshops, BPOE 2014, Salt Lake City, USA, March 1, 2014 and Hangzhou, China, September 5, 2014 : revised selected papers
- Big data computing
- Big data demystified : how to use big data, data science and AI to make better business decisions and gain competitive advantage
- Big data driven supply chain management : a framework for implementing analytics and turning information into intelligence
- Big data for chimps : a guide to massive-scale data processing in practice
- Big data fundamentals : concepts, drivers & techniques
- Big data imperatives : enterprise big data warehouse, BI implementations and analytics
- Big data in cognitive science
- Big data in context : legal, social and technological insights
- Big data now : 2014 edition : current perspectives from O'Reilly Media
- Big data now : 2015 edition : current perspectives from O'Reilly Media
- Big data now : 2016 edition : current perspectives from O'Reilly Media
- Big data now : current perspectives from O'Reilly radar
- Big data, big analytics : emerging business intelligence and analytic trends for today's businesses
- Big data, big analytics : emerging business intelligence and analytic trends for today's businesses
- Big data, data mining and machine learning : value creation for business leaders and practitioners
- Big data, data mining, and machine learning : value creation for business leaders and practitioners
- Big data, open data and data development
- Biological data mining in protein interaction networks
- Biomedical literature mining
- Biomedical literature mining
- Biometric system and data analysis : design, evaluation, and data mining
- Biometric system and data analysis : design, evaluation, and data mining
- Biostatistics Using JMP
- Bisociative knowledge discovery : an introduction to concept, algorithms, tools, and applications
- Boosted statistical relational learners : from benchmarks to data-driven medicine
- Breakthrough Business Analysis
- Building Cognitive Applications with IBM Watson Services : Volume 1 Getting Started
- Building Cognitive Applications with IBM Watson Services : Volume 2 Conversation
- Building Data Science Teams
- Building Twitter bot with Python and Tweepy : Python project
- Building a big data analytics stack
- Building a data mart with Pentaho data integration
- Building an intelligent Web : theory and practice
- Building an intelligent Web : theory and practice
- Building better distributed data pipelines
- Building cognitive applications with IBM Watson services, Volume 1, Getting started
- Building data pipelines with Python : understanding pipeline frameworks, workflow automation, and Python toolsets
- Building pipelines for natural language understanding with Spark : a hands-on guide to machine learning annotators, topic modeling, and deep learning for text mining
- Building the data-driven organization
- Business Intelligence und Reporting mit Microsoft SQL Server 2008 : OLAP, data mining, analysis services, reporting services und integration services mit SQL Server 2008
- Business information : finding and using data in the digital age
- Business intelligence : making decisions through data analytics
- Citizen in sensor networks : second international workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, revised selected papers
- Classification and data mining
- Clinical text mining : secondary use of electronic patient records
- Cloudera Impala
- Cluster analysis for data mining and system identification
- Cluster analysis for data mining and system identification / János Abonyi, Balázs Feil
- Clustering : a data recovery approach
- Clustering and information retrieval
- Clustering in relational data and ontologies
- Collective intelligence in action
- Commercial data mining : processing, analysis and modeling for predictive analytics projects
- Commercial data mining : processing, analysis and modeling for predictive analytics projects
- Communicating data with Tableau
- Competing on analytics : the new science of winning
- Complex networks : second international workshop, CompleNet 2010, Rio de Janeiro, Brazil, October 13-15, 2010, Revised selected papers
- Compression schemes for mining large datasets : a machine learning perspective
- Computation, causation, and discovery
- Computational analysis of terrorist groups : Lashkar-e-Taiba
- Computational complexity : theory, techniques, and applications
- Computational framework for knowledge : integrated behavior of machines
- Computational medicine in data mining and modeling
- Computational methods of feature selection
- Computational movement analysis
- Computational social networks : tools, perspectives and applications
- Computer science for environmental engineering and ecoinformatics : International Workshop, CSEEE 2011, Kunming, China, July 20-31, 2011, proceedings
- Computer vision/computer graphics collaboration techniques : 5th International Conference, MIRAGE 2011, Rocquencourt, France, October 10-11, 2011, proceedings
- Computing with spatial trajectories
- Computing with spatial trajectories
- Contemporary research methods and data analytics in the news industry
- Content extraction, analysis, and retrieval for plant visual traits studies
- Convergence and hybrid information technology : 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011, proceedings
- Coolhunting : chasing down the next big thing
- Core concepts in data analysis : summarization, correlation and visualization
- Cost-Based Pricing
- Counterterrorism and cybersecurity : total information awareness
- Counterterrorism and cybersecurity : total information awareness
- Cracking the data science interview : what to expect and how to succeed
- Creating a culture of data-driven business
- Critical infrastructure protection and the Domain Name Service (DNS) system
- Crowdsourcing geographic knowledge : volunteered geographic information (VGI) in theory and practice
- Customer analytics for dummies
- Customer analytics for dummies
- Customer and business analytics : applied data mining for business decision making using R
- Customer segmentation and clustering using SAS Enterprise Miner
- Customer segmentation and clustering using SAS Enterprise miner
- DB2 10.5 with BLU Acceleration : new dynamic in-memory analytics for the era of big data
- Dark Web : exploring and data mining the dark side of the web
- Dark web : exploring and data mining the dark side of the web
- Dark web : exploring and data mining the dark side of the web
- Data Analysis in the Cloud : Models, Techniques and Applications
- Data Jujitsu
- Data Mining mit Microsoft SQL Server : Analyse und Mustererkennung in Daten mit Excel 2007 und SQL Server 2005/2008
- Data Science : Grundlagen, Architekturen und Anwendungen
- Data Science mit Python : das Handbuch für den Einsatz von IPython, Jupyter, NumPy, Pandas, Matplotlib, Scikit-Learn
- Data analysis and data mining : an Introduction
- Data analysis and decision support
- Data analysis and decision support
- Data analysis and visualization using Python : analyze data to create visualizations for BI systems
- Data analysis on streams
- Data analysis using SQL and Excel
- Data analysis with Python
- Data analytics : models and algorithms for intelligent data analysis
- Data analytics for corporate debt markets : using data for investing, trading, capital markets, and portfolio management
- Data analytics using Spark and Hadoop : learn how to integrate Spark and Hadoop in a series of hands-on labs
- Data and Text Processing for Health and Life Sciences
- Data and text mining : a business applications approach
- Data bootcamp
- Data clustering : algorithms and applications
- Data clustering : algorithms and applications
- Data clustering in C++ : an object-oriented approach
- Data driven : creating a data culture
- Data engineering : mining, information and intelligence
- Data infrastructure for next-gen finance
- Data infrastructure for next-gen finance : tools for cloud migration, customer even hubs, governance & security
- Data integration in the life sciences : proceedings
- Data just right : introduction to large-scale data & analytics
- Data just right LiveLessons
- Data marketplaces
- Data matching : concepts and techniques for record linkage, entity resolution, and duplicate detection
- Data mining : a heuristic approach
- Data mining : a knowledge discovery approach
- Data mining : a knowledge discovery approach
- Data mining : concepts and techniques
- Data mining : concepts and techniques
- Data mining : concepts and techniques
- Data mining : concepts, models, methods, and algorithms
- Data mining : know it all
- Data mining : know it all
- Data mining : multimedia, soft computing, and bioinformatics
- Data mining : next generation challenges and future directions
- Data mining : opportunities and challenges
- Data mining : practical machine learning tools and techniques
- Data mining : practical machine learning tools and techniques
- Data mining : practical machine learning tools and techniques
- Data mining : practical machine learning tools and techniques
- Data mining : special issue in Annals of information systems
- Data mining : technologies, techniques, tools, and trends
- Data mining : theories, algorithms, and examples
- Data mining : theory, methodology, techniques, and applications
- Data mining : theory, methodology, techniques, and applications
- Data mining algorithms in C++ : data patterns and algorithms for modern applications
- Data mining and applications in genomics
- Data mining and applications in genomics
- Data mining and computational intelligence
- Data mining and data warehousing
- Data mining and decision support : integration and collaboration
- Data mining and diagnosing IC fails
- Data mining and knowledge discovery approaches based on rule induction techniques
- Data mining and knowledge discovery for big data : methodologies, challenge and opportunities
- Data mining and knowledge discovery for geoscientists
- Data mining and knowledge discovery for process monitoring and control
- Data mining and knowledge discovery handbook
- Data mining and knowledge discovery via logic-based methods : theory, algorithms, and applications
- Data mining and knowledge discovery via logic-based methods : theory, algorithms, and applications
- Data mining and knowledge discovery with evolutionary algorithms
- Data mining and learning analytics : applications in educational research
- Data mining and learning analytics : applications in educational research
- Data mining and learning analytics : applications in educational research
- Data mining and multi-agent integration
- Data mining and predictive analysis : intelligence gathering and crime analysis
- Data mining and predictive analytics
- Data mining and predictive analytics
- Data mining and statistical analysis of completions in the Canadian Montney formation
- Data mining and statistical analysis using SQL
- Data mining and uncertain reasoning : an integrated approach
- Data mining applications using artificial adaptive systems
- Data mining extension for economics
- Data mining for association rules and sequential patterns : sequential and parallel algorithms
- Data mining for bioinformatics
- Data mining for biomarker discovery
- Data mining for business applications
- Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner
- Data mining for design and manufacturing : methods and applications
- Data mining for dummies
- Data mining for genomics and proteomics : analysis of gene and protein expression data
- Data mining for geoinformatics : methods and applications
- Data mining for scientific and engineering applications
- Data mining for systems biology : methods and protocols
- Data mining for systems biology : methods and protocols
- Data mining for the social sciences : an introduction
- Data mining in agriculture
- Data mining in bioinformatics
- Data mining in bioinformatics
- Data mining in biomedicine using ontologies
- Data mining in crystallography
- Data mining in crystallography
- Data mining in finance : advances in relational and hybrid methods
- Data mining in large sets of complex data
- Data mining in proteomics : from standards to applications
- Data mining in time series databases
- Data mining introductory and advanced topics
- Data mining methods and models
- Data mining methods for knowledge discovery
- Data mining methods for the content analyst : an introduction to the computational analysis of content
- Data mining methods for the content analyst : an introduction to the computational analysis of content
- Data mining mobile devices
- Data mining on multimedia data
- Data mining techniques : for marketing, sales, and customer relationship management
- Data mining techniques : for marketing, sales, and customer relationship management
- Data mining techniques for the life sciences
- Data mining techniques for the life sciences
- Data mining techniques in CRM : inside customer segmentation
- Data mining techniques in grid computing environments
- Data mining techniques in sensor networks : summarization, interpolation and surveillance
- Data mining with Microsoft SQL server 2008
- Data mining with Rattle and R : the art of excavating data for knowledge discovery
- Data mining with Rattle and R : the art of excavating data for knowledge discovery
- Data mining with computational intelligence
- Data mining with computational intelligence
- Data mining with decision trees : theory and applications
- Data mining your website
- Data mining, rough sets, and granular computing
- Data mining-aided crystal engineering for the design of transparent conducting oxides : preprint
- Data munging with Hadoop
- Data preparation for analytics using SAS
- Data science & big data analytics : discovering, analyzing, visualizing and presenting data
- Data science : concepts and practice
- Data science and big data analytics : discovering, analyzing, visualizing and presenting data
- Data science and machine learning with Python - hands on!
- Data science and machine learning with Python--Hands on!
- Data science at the command line
- Data science for business
- Data science for dummies
- Data science for dummies
- Data science for dummies
- Data science from scratch : first principles with Python
- Data science fundamentals for Python and MongoDB
- Data science strategy
- Data science with Python : combine Python with machine learning principles to discover hidden patterns in raw data
- Data science with Python : unleash the power of Python and its robust data science capabilities : a course in four modules