데이터 마이닝

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  • This can be done by data mining, or by developing hypotheses and testing them using analytic tools such as OLAP.[1]
  • The analytic sector of BI can be broken down into two general areas: query and analysis and data mining.[1]
  • Data mining seeks trends within the data, which may be used for later analysis.[1]
  • Data mining and KDD are concerned with extracting models and patterns of interest from large databases.[1]
  • Data mining is just a more developed practice that has come about since the beginning of the 20th century.[2]
  • Although data mining is more commonly used for analyzing big data sets, it can be used for any size.[2]
  • There will always be an instance in which data mining might not be worth the return on investment.[2]
  • While analysis and visualization tools become ever more user-friendly, more people are now able to gain insights from data mining.[2]
  • Data mining lies at the heart of many of these questions, and the research done at Google is at the forefront of the field.[3]
  • From this need, the research filed of data mining emerged.[4]
  • Data mining is used for many purposes, depending on the company and its needs.[5]
  • Many people view data mining, or big data, as machine learning.[6]
  • The process of discovering these patterns in data is called data mining.[7]
  • For example, what areas of business do you want to improve through data mining?[7]
  • This data mining technique organizes similar and dissimilar items together.[7]
  • Data mining is helping advertisers identify look-alike customers, so they can target them with tailored ads and promotions.[7]
  • The process of data mining is simple and consists of three stages.[8]
  • Data mining provides several techniques to help organizations classify this data and find patterns or relationships between data pieces.[9]
  • In fact, this concern is misplaced in many ways because data mining in and of itself has a limited ability, if any, to compromise privacy.[10]
  • On the other hand, some have suggested that incorporation of data mining and predictive analytics might result in a waste of resources.[10]
  • We do not need data mining or technology to make errors; we have been able to do that without the assistance of technology for many years.[10]
  • Data mining can only be used on the data that are made available to it.[10]
  • Data mining is the process of understanding data through cleaning raw data, finding patterns, creating models, and testing those models.[11]
  • Data mining often includes multiple data projects, so it’s easy to confuse it with analytics, data governance, and other data processes.[11]
  • As discussed, data mining may be confused with other data projects.[11]
  • The Cross-Industry Standard Process for Data Mining (CRISP-DM) is an excellent guideline for starting the data mining process.[11]
  • Data mining occurs in several steps, starting with data collection and storage.[12]
  • Most importantly, data mining can be used to perform several specific analyses on information.[12]
  • For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions.[13]
  • Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.[13]
  • Automated prediction of trends and behaviors: Data mining automates the process of finding predictive information in a large database.[13]
  • Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings.[13]
  • Clearly, therefore, data mining is a strategy, not a guaranteed solution, but, equally clearly, delivers a lot more than one query.[14]
  • In simple words, data mining is defined as a process used to extract usable data from a larger set of any raw data.[15]
  • Data mining involves effective data collection and warehousing as well as computer processing.[15]
  • For segmenting the data and evaluating the probability of future events, data mining uses sophisticated mathematical algorithms.[15]
  • No matter what your level of expertise, you will be able to find helpful books and articles on data mining.[16]
  • Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis.[16]
  • Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events.[16]
  • Grouping Other forms of data mining identify natural groupings in the data.[16]
  • Data mining isn’t a new invention that came with the digital age.[17]
  • Data mining can answer business questions that traditionally were too time consuming to resolve manually.[17]
  • Data mining is used in many areas of business and research, including sales and marketing, product development, healthcare, and education.[17]
  • The future is bright for data mining and data science as the amount of data will only increase.[17]
  • Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn’t coined until the 1990s.[18]
  • The predictive capacity of data mining has changed the design of business strategies.[19]
  • Data mining is used to explore increasingly large databases and to improve market segmentation.[19]
  • Banks use data mining to better understand market risks.[19]
  • There are networks that apply real time data mining to measure their online television (IPTV) and radio audiences.[19]
  • Data mining is the process of analyzing a data set to find insights.[20]
  • Learn about different applications of data mining and get experience working with data mining algorithms.[20]
  • The data mining tutorial provides basic and advanced concepts of data mining.[21]
  • Data mining is also called Knowledge Discovery in Database (KDD).[21]
  • Data mining utilizes complex mathematical algorithms for data segments and evaluates the probability of future events.[21]
  • Data Mining is also called Knowledge Discovery of Data (KDD).[21]
  • As Han and Kamber1 point out, the term ‘data mining’ is a misnomer.[22]
  • Thus, data mining should be better named ‘knowledge mining from data’ or simply ‘knowledge mining’.[22]
  • Data mining of large databases involves more stages and more complex algorithms than simple data exploration.[22]
  • Some goals of data mining include supervised classification and regression.[22]
  • Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets.[23]
  • Data transformation operations change the data to make it useful in data mining.[23]
  • E-Commerce E-commerce websites use Data Mining to offer cross-sells and up-sells through their websites.[23]
  • Super Markets Data Mining allows supermarket's develope rules to predict if their shoppers were likely to be expecting.[23]
  • Data mining is deprecated in SQL Server Analysis Services 2017.[24]
  • Data mining is the process of discovering actionable information from large sets of data.[24]
  • Data mining uses mathematical analysis to derive patterns and trends that exist in data.[24]
  • Microsoft SQL Server Data Mining provides an integrated environment for creating and working with data mining models.[24]
  • Data mining is a process used by companies to turn raw data into useful information.[25]
  • To illustrate, imagine a restaurant wants to use data mining to determine when it should offer certain specials.[25]
  • The term data mining appeared around 1990 in the database community, generally with positive connotations.[26]
  • researchers consequently turned to data mining.[26]
  • Pre-processing is essential to analyze the multivariate data sets before data mining.[26]
  • Data mining is used wherever there is digital data available today.[26]

소스

  1. ↑ 1.0 1.1 1.2 1.3 What is Data Analysis and Data Mining?
  2. ↑ 2.0 2.1 2.2 2.3 5 Myths of Data Mining
  3. ↑ Data Mining and Modeling – Google Research
  4. ↑ What Is Data Mining and How Does It Work?
  5. ↑ What is Data Mining?
  6. ↑ The 10 data mining techniques data scientists need for their toolbox
  7. ↑ 7.0 7.1 7.2 7.3 What Is Data Mining? Explanation of Data Mining
  8. ↑ Data Mining Definition in Terms of BI
  9. ↑ Data Mining
  10. ↑ 10.0 10.1 10.2 10.3 Data Mining - an overview
  11. ↑ 11.0 11.1 11.2 11.3 How data mining works: a guide
  12. ↑ 12.0 12.1 Data Mining Basics - What is Data Mining?
  13. ↑ 13.0 13.1 13.2 13.3 Data Mining
  14. ↑ Data Mining - an overview
  15. ↑ 15.0 15.1 15.2 What is Data Mining? Definition of Data Mining, Data Mining Meaning
  16. ↑ 16.0 16.1 16.2 16.3 What Is Data Mining?
  17. ↑ 17.0 17.1 17.2 17.3 What is Data Mining? Definition and Examples
  18. ↑ What is data mining?
  19. ↑ 19.0 19.1 19.2 19.3 DATA MINING: DEFINITION, EXAMPLES AND APPLICATIONS
  20. ↑ 20.0 20.1 Learn Data Mining with Online Courses and Lessons
  21. ↑ 21.0 21.1 21.2 21.3 Data Mining Tutorial
  22. ↑ 22.0 22.1 22.2 22.3 Data Mining - an overview
  23. ↑ 23.0 23.1 23.2 23.3 Data Mining Tutorial: Process, Techniques, Tools, EXAMPLES
  24. ↑ 24.0 24.1 24.2 24.3 데이터 마이닝 개념
  25. ↑ 25.0 25.1 Data Mining Definition
  26. ↑ 26.0 26.1 26.2 26.3 Data mining

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'data'}, {'LEMMA': 'mining'}]
  • [{'LOWER': 'data'}, {'LEMMA': 'discovery'}]
  • [{'LOWER': 'knowledge'}, {'LEMMA': 'discovery'}]
  • [{'LEMMA': 'datamine'}]
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