About Data mining
Data Mining Notes for Students PDFFree Data Mining notes pdf are provided here for Data Mining students so that they can prepare and score high marks in their Data Mining exam.In these Data Mining free notes pdf, we will introduce data mining techniques and enables you to apply these techniques on real-life datasets. These notes focus on three main data mining techniques: Classification, Clustering, and Association Rule Mining tasks.We have provided complete Data Mining Handwritten notes pdf for any university student of BCA, MCA, B.Sc, B.Tech CSE, M.Tech branch to enhance more knowledge about the subject and to score better marks in their Data Mining exam.Free Data Mining notes pdf are very useful for Data Mining students in enhancing their preparation and improving their chances of success in Data Mining exam.These Data Mining free notes pdf will help students tremendously in their preparation for Data Mining exam. Please help your friends in scoring good marks by sharing these Data Mining free pdf notes from below links:Topics in our Data Mining Handwritten Notes PDFThe topics we will cover in these Data Mining Handwritten Notes PDF will be taken from the following list:Introduction to Data Mining: Applications of data mining, data mining tasks, motivation and challenges, types of data attributes and measurements, data quality.Data Pre-Processing: Aggregation, sampling, dimensionality reduction, Feature Subset Selection, Feature Creation, Discretization and Binarization, Variable Transformation.Classification: Basic Concepts, Decision Tree Classifier: Decision tree algorithm, attribute selection measures, Nearest Neighbour Classifier, Bayes Theorem, and Naive Bayes Classifier,Model Evaluation: Holdout Method, Random Sub Sampling, Cross-Validation, evaluation metrics, confusion matrix.Association rule mining: Transaction data-set, Frequent Itemset, Support measure, Apriori Principle, Apriori Algorithm, Computational Complexity, Rule Generation, Confidence of association rule.Cluster Analysis: Basic Concepts, Different Types of Clustering Methods, Different Types of ClustersK-means: The Basic K-means Algorithm, Strengths and Weaknesses of K-means algorithmAgglomerative Hierarchical Clustering: Basic Algorithm, Proximity between clustersDBSCAN: The DBSCAN Algorithm, Strengths, and Weaknesses.Data Mining Notes PDF FREE DownloadData Mining students can easily make use of all these complete Data Mining Handwritten notes pdf by downloading them from below links:Data Mining Notes by Abhishek.pdfData Mining Handwritten Notes by Aditi.pdfData Mining Handwritten Notes by Ambika.pdfData Mining Handwritten Notes by Deepanshu.pdfData Mining Handwritten Notes by Riya.pdfData Mining and Data Warehousing Notes.pdfData mining notes for bsc computer scienceSource: nptel.ac.inData mining notes pdf downloadSource: iitr.ac.injntuh data mining notes pdfSource: iitd.ac.inData mining lecture notesSource: iare.ac.inData Mining Notes for BSc Computer ScienceSource: ocw.mit.eduData Mining Lecture Notes PDFSource: slideshare.netHow to Download FREE Data Mining Notes PDF?Data Mining students can easily download free Data Mining notes pdf by following the below steps:Visit TutorialsDuniya.com to download Data Mining free notes pdfSelect ‘College Notes’ and then select ‘Computer Science Course’Select ‘Data Mining Notes’Now, you can easily view or download free Data Mining pdf notesData Mining BooksWe have listed the best Data Mining Books that can help in your Data Mining exam preparation:Benefits of FREE Data Mining Notes PDFFree Data Mining notes pdf provide learners with a flexible and efficient way to study and reference Data Mining concepts. Benefits of these complete
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Browse Presentation Creator Pro Upload Dec 20, 2019 350 likes | 993 Views Data Mining: Concepts and Techniques. Introduction. Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems Major issues in data mining. Why Data Mining?. Download Presentation Data Mining: Concepts and Techniques An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher. Presentation Transcript Data Mining: Concepts and TechniquesIntroduction • Motivation: Why data mining? • What is data mining? • Data Mining: On what kind of data? • Data mining functionality • Are all the patterns interesting? • Classification of data mining systems • Major issues in data miningWhy Data Mining? • The Explosive Growth of Data: from terabytes to petabytes • Data collection and data availability • Automated data collection tools, database systems, Web, computerized society • Major sources of abundant data • Business: Web, e-commerce, transactions, stocks, … • Science: Remote sensing, bioinformatics, scientific simulation, … • Society and everyone: news, digital cameras, • We are drowning in data, but starving for knowledge! • “Necessity is the mother of invention”—Data mining—Automated analysis of massive data setsEvolution of Database Technology • 1960s: • Data collection, database creation, IMS and network DBMS • 1970s: • Relational data model, relational DBMS implementation • 1980s: • RDBMS, advanced data models (extended-relational, OO, deductive, etc.) • Application-oriented DBMS (spatial, scientific, engineering, etc.) • 1990s: • Data mining, data warehousing, multimedia databases, and Web databases • 2000s • Stream data management and mining • Data mining and its applications • Web technology (XML, data integration) and global information systemsWhat Is Data Mining? • Data mining (knowledge discovery from data) • Extraction of interesting (non-trivial,implicit, previously unknown and potentially useful)patterns or knowledge from huge amount of data • Alternative name • Knowledge discovery in databases (KDD) • Watch out: Is everything “data mining”? • Query processing • Expert systems or statistical programsWhy Data Mining?—Potential Applications • Data analysis and decision support • Market analysis and management • Target marketing, customer relationship management (CRM), market basket analysis, market segmentation •
