Data mining

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Statistics Definitions Data Mining Contents: What is Data Mining? Steps in Data Mining Data sets in Data Mining. What is Data Mining? Data mining, or knowledge discovery from data Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or a Data Warehouse System, Major issues in Data Mining. Data Preprocessing: Need for Preprocessing the Data, Data Cleaning, Data Integration and

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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 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. 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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 •

Data Mining: Data Mining Concepts and Techniques

The discovered knowledge with existing one: knowledge fusion Major Issues in Data Mining • User interaction • Data mining query languages and ad-hoc mining • Expression and visualization of data mining results • Interactive mining ofknowledge at multiple levels of abstraction • Applications and social impacts • Domain-specific data mining & invisible data mining • Protection of data security, integrity, and privacySummary • Data mining: discovering interesting patterns from large amounts of data • A natural evolution of database technology, in great demand, with wide applications • A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation • Mining can be performed in a variety of information repositories • Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. • Data mining systems and architectures • Major issues in data miningWhere to Find References? • More conferences on data mining • PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc. • Data mining and KDD • Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. • Journal: Data Mining and Knowledge Discovery, KDD Explorations • Database systems • Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA • Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc. • AI & Machine Learning • Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), etc. • Journals: Machine Learning, Artificial Intelligence, etc. • Statistics • Conferences: Joint Stat. Meeting, etc. • Journals: Annals of statistics, etc. • Visualization • Conference proceedings: CHI, ACM-SIGGraph, etc. • Journals: IEEE Trans. visualization and computer graphics, etc.. Statistics Definitions Data Mining Contents: What is Data Mining? Steps in Data Mining Data sets in Data Mining. What is Data Mining? Data mining, or knowledge discovery from data

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Contrast data characteristics • Association (correlation and causality) • Diaper à Beer [0.5%, 75%] • Classification and Prediction • Construct models (functions) that describe and distinguish classes or concepts for future prediction • Presentation: decision-tree, classification rule, neural networkData Mining Functionalities • Cluster analysis • Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns • Maximizing intra-class similarity & minimizing interclass similarity • Outlier analysis • Outlier: a data object that does not comply with the general behavior of the data • Useful in fraud detection, rare events analysis • Trend and evolution analysis • Trend and deviation: regression analysis • Sequential pattern mining, periodicity analysisAre All the “Discovered” Patterns Interesting? • Data mining may generate thousands of patterns: Not all of them are interesting • Suggested approach: Human-centered, query-based, focused mining • Interestingness measures • A pattern is interesting if it is easily understood by humans, validon newor test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm • Objective vs. subjective interestingness measures • Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. • Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty.Data Mining: Confluence of Multiple Disciplines Database Systems Statistics Data Mining Machine Learning Visualization Algorithm Other DisciplinesData Mining: Classification Schemes • Different views, different classifications • Kinds of data to be mined • Kinds of knowledge to be discovered • Kinds of techniques utilized • Kinds of applications adaptedMulti-Dimensional View of Data Mining • Data to be mined • Relational, data warehouse, transactional, stream, object-oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, WWW • Knowledge to be mined • Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. • Multiple/integrated functions and mining at multiple levelsMulti-Dimensional View of Data Mining • Techniques utilized • Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. • Applications adapted • Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, Web mining, etc.OLAP Mining: Integration of Data Mining and Data Warehousing • Data mining systems, DBMS, Data warehouse systems coupling • On-line analytical mining data • Integration of mining and OLAP technologies • Interactive mining multi-level knowledge • Necessity of mining knowledge and patterns at different levels of abstraction. • Integration of multiple mining functions • Characterized classification, first clustering and then associationMajor Issues in Data Mining • Mining methodology • Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web • Performance: efficiency, effectiveness, and scalability • Pattern evaluation: the interestingness problem • Incorporation of background knowledge • Handling noise and incomplete data • Parallel, distributed and incremental mining methods • Integration of We are living in an information-rich, data-driven world. While it’s comforting to know there’s a plethora of readily available knowledge, the sheer volume creates challenges. The more information available, the longer it can find the useful insights you need.That’s why today we’re discussing data mining. We’ll be exploring all aspects of data mining, including what it means, its stages, data mining techniques, the benefits it offers, data mining tools, and more. Let's get started and learn what is data mining.What is Data Mining?Data mining is analyzing enormous amounts of information and datasets, extracting (or “mining”) helpful intelligence to help organizations solve problems, predict trends, mitigate risks, and find new opportunities. Data mining is like actual mining because, in both cases, the miners are sifting through mountains of material to find valuable resources and elements.Data mining also includes establishing relationships and finding patterns, anomalies, and correlations to tackle issues, creating actionable information in the process. It is a wide-ranging and varied process that includes many different components, some of which are even confused with data mining itself. Data Mining StepsNow that you have a hang of what is data mining, let's look at the steps involved. Data mining is a multi-step process that involves extracting valuable information from large data sets. Here are the detailed steps involved in data mining:1. Understanding and Guaging DataThe first step in the data mining process is knowing your data. You must thoroughly understand the data to identify its characteristics, quality, and relevance. You must also gauge its structure, volume, and nature and determine its relevance to the business objectives.2. Data PreparationThe next step in the data mining process is data preparation. You must start preparing the data for mining by cleaning, transforming, and selecting relevant data. Here’s all about it in detail.Data Cleaning: In this step, you should remove noise, handle missing values, and correct errors.Data Integration: This step includes combining data from different sources into a coherent data set.Data Transformation: Normalize or aggregate data to ensure consistency and improve mining results.Data Reduction: Reduce the data volume by selecting only relevant features, creating new features, or sampling.3. Data SelectionThe next step in the overall data mining process is data selection. You must define criteria for selecting relevant data and extract the appropriate subset of data for mining4. Data MiningNext up: data mining! You should apply data mining techniques to extract patterns and insights from the prepared data. You should choose appropriate data mining techniques, such as classification, clustering, and regression, and apply them to the data. Once you have done this, perform iterative testing and validation to refine the mining process.5. Pattern Evaluation and PresentationVisualize patterns and insights using charts, graphs, and dashboards, and prepare reports to communicate your findings. And then present the mined knowledge in an actionable format. (Earn a brownie point by also interpreting your findings in the context of business objectives.)Examples of Data MiningThe following are a few real-world examples of data:Shopping Market AnalysisIn the shopping market, there is a big

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Data mining is not an easy task, as the algorithms used can get very complex and data is not always available at one place. It needs to be integrated from various heterogeneous data sources. These factors also create some issues. Here in this tutorial, we will discuss the major issues regarding −Mining Methodology and User InteractionPerformance IssuesDiverse Data Types IssuesThe following diagram describes the major issues.Mining Methodology and User Interaction IssuesIt refers to the following kinds of issues −Mining different kinds of knowledge in databases − Different users may be interested in different kinds of knowledge. Therefore it is necessary for data mining to cover a broad range of knowledge discovery task.Interactive mining of knowledge at multiple levels of abstraction − The data mining process needs to be interactive because it allows users to focus the search for patterns, providing and refining data mining requests based on the returned results.Incorporation of background knowledge − To guide discovery process and to express the discovered patterns, the background knowledge can be used. Background knowledge may be used to express the discovered patterns not only in concise terms but at multiple levels of abstraction.Data mining query languages and ad hoc data mining − Data Mining Query language that allows the user to describe ad hoc mining tasks, should be integrated with a data warehouse query language and optimized for efficient and flexible data mining.Presentation and visualization of data mining results − Once the patterns are discovered it needs to be expressed in high level languages, and visual representations. These representations should be easily understandable.Handling noisy or incomplete data − The data cleaning methods are required to handle the noise and incomplete objects while mining the data regularities. If the data cleaning methods are not there then the accuracy of the discovered patterns will be poor.Pattern evaluation − The patterns discovered should be interesting because either they represent common knowledge or lack novelty.Performance IssuesThere can be performance-related issues such as follows −Efficiency and scalability of data mining algorithms − In order to effectively extract the information from huge amount of data in databases, data mining algorithm must be efficient and scalable.Parallel, distributed, and incremental mining algorithms − The factors such as huge size of databases, wide distribution of data, and complexity of data mining methods motivate the development of parallel and distributed data mining algorithms. These algorithms divide the data into partitions which is further processed in a parallel fashion. Then the results from the partitions is merged. The incremental algorithms, update databases without mining the data again from scratch.Diverse Data Types IssuesHandling of relational and complex types of data − The database may contain complex data objects, multimedia data objects, spatial data, temporal

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Statistics, Inferential Statistics, and more8+ skills includingSupervised & Unsupervised LearningDeep LearningData Visualization, and moreAdditional BenefitsApplied Learning via Capstone and 25+ Data Science ProjectsPurdue Alumni Association MembershipFree IIMJobs Pro-Membership of 6 monthsResume Building AssistanceUpto 14 CEU Credits Caltech CTME Circle MembershipCost$$$$$$$$$$Explore ProgramExplore ProgramExplore ProgramThere’s a lot of data generated every day, and consequently, there is a correspondingly great demand for professionals to analyze that information using techniques like data mining. Simplilearn’s Caltech Post Graduate Program in Data Science is the perfect data analytics certification course for anyone on a data scientist career path. This program, held in partnership with Purdue University and collaboration with IBM, gives you broad exposure to key technologies and skills currently used in data analytics and data science. You will learn statistics, Python, R, Tableau, SQL, and Power BI. Once you complete this comprehensive data analytics course, you will be ready to take on a professional data analytics role.Upskill yourself with our trending Data Science Courses and CertificationsData Science CourseProfessional Certificate Course in Data ScienceProfessional Certificate in Data Science and Generative AIFAQs1. Why use data mining?Data mining uses span from the finance industry searching for market patterns to governments attempting to uncover potential security risks. Corporations, particularly internet and social media businesses, mine user data to build successful advertising and marketing campaigns targeting certain consumer groups.2. Why is data mining so popular?The reason is simple: it creates several commercial prospects because to its predictive and descriptive capabilities; hence, it is the technology that can forecast the future and make it lucrative. Businesses may learn more about their consumers by utilizing software to search for patterns in enormous amounts of data. This allows them to design successful marketing campaigns, improve sales, and save expenses.3. What are the key advantages of data mining?It assists organizations in making informed judgments.4. What are the disadvantages of Data Mining?Data mining makes extensive use of technology in the data collecting process. Every piece of data created needs its own storage space as well as upkeep. This can significantly raise the cost of deployment. When employing data mining, identity theft is a major concern. If proper security is not given, it may expose security vulnerabilities. 5. What are the types of data mining?There are two types of Data Mining: Predictive Data Mining Analysis and Descriptive Data Mining Analysis.6. What are the advantages and disadvantages of Data Mining?AdvantagesIt aids in the detection of hazards and fraud.It aids in the understanding of behaviors, trends and the discovery of hidden patterns.Aids in the rapid analysis of vast amounts of dataDisadvantagesData mining necessitates vast datasets and is costly.7. How is data mining done?Projects such as data cleansing and exploratory analysis are part of the data mining process, but they are not the only ones. Data mining professionals clean and prepare data, develop models, test models against hypotheses, and publish models for analytics or business intelligence initiatives.8. What is another term for Data Mining?Knowledge Discovery in Data(KDD) is another name for data mining.9. Where is Data Mining used?Market risks. Statistics Definitions Data Mining Contents: What is Data Mining? Steps in Data Mining Data sets in Data Mining. What is Data Mining? Data mining, or knowledge discovery from data

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Data mining, sometimes called knowledge discovery, is the process of sifting large volumes of data for correlations, patterns, and trends. Data mining definitionData mining, sometimes used synonymously with “knowledge discovery,” is the process of sifting large volumes of data for correlations, patterns, and trends. It is a subset of data science that uses statistical and mathematical techniques along with machine learning and database systems. The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SigKDD) defines it as the science of extracting useful knowledge from the huge repositories of digital data created by computing technologies.The idea of extracting patterns from data is not new, but the modern concept of data mining began taking shape in the 1980s and 1990s with the use of database management and machine learning techniques to augment manual processes.Data mining vs. data analyticsThe terms data analytics and data mining are often conflated, but data analytics can be understood as a subset of data mining. Data mining focuses on cleaning raw data, finding patterns, creating models, and then testing those models, according to analytics vendor Tableau. Data analytics, on the other hand, is the part of data mining focused on extracting insights from data. Its aim is to apply statistical analysis and technologies on data to find trends and solve problems. The business value of data miningData mining is used at companies across a broad swathe of industries to sift through their data to understand trends and make better business decisions. Media and