data mining in research methodology ppt


Here are 10 data mining techniques that we will explore in detail: If youre interested in pursuing a data science career, read on to learn more about these data mining methods and how they can lead to success in different industries. > N `! R[" 6 v 8= `!3 xZ \?g~wH=}\rOE4n).=eff&->s,sGj=+54eRY/709^Ssg~93? "@context": "http://schema.org", Data is copied and moved from its source to a warehouse staging area. Powered by Trilogy Education Services, a 2U, Inc. brand. "contentUrl": "https://slideplayer.com/slide/5798869/19/images/3/What+is+Data+Mining.jpg", "description": "Statistics. /Group <> Natural data deviations can be instructive as well. Data warehousing and on-line analytical processing. @:eJJsL>%|;~JNizu Advanced Database Applications Database Indexing and Data Mining CS591-G1 -- Fall 2001 George Kollios Boston University. stream CIT 858: Data Mining and Data Warehousing Course Instructor: Bajuna Salehe Web: Data Mining: Concepts & Techniques. Data can be presented in visual ways through charts, graphs, maps, diagrams, and more. By definition is the process of extracting previously unknown data from large databases and using it to make orgnisational decisions. Request information about the Data Science and Analytics Boot Camp to learn more about machine learning and other topics covered in the program curriculum. "@context": "http://schema.org", endobj For retailers, its particularly helpful in making purchasing suggestions. iraZa5INw\]wBoJX^4;Oq&kX>AMJ=| yn.g[KG8 ffAKk10Z\w'm7LHg6/>Wa) Computers process large amounts of data much faster than human brains but dont yet have the capacity to apply common sense and imagination in working with the data. `:5%vq7xVQ@Gc~^VOGj e+;*@||HE-.y4\Z=h,p :kDPj.R9m=)]Y}OSW,ZrzjttS^,:C\. 6 0 obj endobj As a result, its a good idea for aspiring data professionals to consider learning such skills through a. /Group <> "@type": "ImageObject", Predictive modeling uses some of the same techniques and terminology as other data mining processes. Risk analysis and management. stream While other data mining methods seek to identify patterns and trends, outlier detection looks for the unique: the data point or points that differ from the rest or diverge from the overall sample. B@!PK%P3}xS+|gq 4w&BTbo8O4Nk7Yvrs +*VXNH6<8B@!P( "description": "User interaction. Data warehousing also consolidates various data sources into one place, making mining and decision-making more efficient and saving businesses time and money. Data Mining FunctionalitiesCluster analysis Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Outlier analysis Outlier: a data object that does not comply with the general behavior of the data Noise or exception? According to a MicroStrategy survey, 18 percent of analytics professionals said machine learning and AI will have the most significant impact on their strategies over the next five years. xWMs6W v7>uNI4) Fraud Detection & Mining Unusual PatternsApplications: Health care, retail, credit card service, telecommunications Auto insurance: ring of collisions Money laundering: suspicious monetary transactions Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or week. /Group <> Its roots have been traced to the 1930s, according to Hacker Bits, but the term became more widely used in the 1990s as businesses attempted to grapple with the ever-increasing amount of data our society was producing to derive value from it. stream Data points outside that range are considered outliers. These diagrams show how data points relate to each other by using a series of lines (or links) to connect objects together. <>>>] Multidimensional summary reports. OLAM and Data Mining: Concepts and Techniques. useful in fraud detection and rare event analysis Other pattern-directed or statistical analyses As we continue to produce a growing amount of diverse data, the ability to mine that data for insights will become increasingly important. Chapter 1. Data cleaning is the process of preparing data to be mined. ?9. }jM6-9=/G_ZgK419Dq.2m(c$cs$is\wyG ]#)uL*zpx#BS6lJ:jddh_#SD : Data Mining is the process of selecting, exploring and modeling large amounts of data to uncover previously unknown. /Contents 16 0 R>> 6B@xn'"P#:30-YvQ*Q)yrUbOHWF]k~F[lOx*bN&lzn H1 To make this website work, we log user data and share it with processors. "description": "By definition is the process of extracting previously unknown data from large databases and using it to make orgnisational decisions. Also data archaeology, information harvesting, information discovery, knowledge extraction, data\/pattern analysis, etc. 18 0 obj Data miners use association to discover unique or interesting relationships between variables in databases. Analyze patterns that deviate from an expected norm. ", "@type": "ImageObject", What data mining techniques should I learn? "@type": "ImageObject", "@type": "ImageObject", "name": "Market Analysis And Management", Learning more. Today, this is typically accomplished through effective, visually accessible mediums such as graphs, 3D models, and even augmented reality. Two primary approaches using association in data mining are the single-dimensional and multi-dimensional methods. CS-470: Data Mining Fall Organizational Details Class Meeting: 4:00-6:45pm, Tuesday, Room SCIT215 Instructor: Dr. Igor Aizenberg Office: Science. "contentUrl": "https://slideplayer.com/slide/5798869/19/images/12/Issues+and+Challenges+of+Data+Mining.jpg", "description": "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. { To use this website, you must agree to our, 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. "width": "800" They also can store and analyze a wide variety of data points, even social media posts about products and businesses. { What is Data Mining By definition is the process of extracting previously unknown data from large databases and using it to make orgnisational decisions. Competition. "description": "Applications: Health care, retail, credit card service, telecommunications. Data mining, the process of sifting through massive amounts of data to identify hidden business trends or patterns, makes these transformational business insights possible. <>>>] Examples of Outlier Detection in Business. "width": "800" "name": "What is Data Mining", What is Data Mining/KDDData mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Associations\/co-relations between product sales, & prediction based on such association. "width": "800" Identifying the best products for different customers. According to MicroStrategy, 52 percent of global businesses consider advanced and predictive modeling their top priority in analytics. ", stream Classification is a fundamental technique in data mining and can be applied to nearly every industry. The data is classified into Yes and No decision trees, with other questions to be asked afterward in a similar fashion. "width": "800" DNA and bio-data analysis. "description": "Data Mining", When computers handle unlabeled data, they engage in unsupervised learning. 14 0 obj Businesses collect and store an unimaginable amount of data, but how do they turn all that data into insights that help them build a better business? 15 0 obj Construct models that describe and distinguish classes or concepts for future prediction. "name": "Why we Need Data Mining Data explosion problem", "@context": "http://schema.org", ", <>>>] "contentUrl": "https://slideplayer.com/slide/5798869/19/images/6/Data+Mining+Functionalities.jpg", Solution: Data warehousing and data mining. According to the U.S. Bureau of Labor Statistics, employment for computer and information research scientists is expected to climb by 15 percent through 2029. This is a more layered process in which computers learn to make decisions based on examining data in a specific environment. Is useful in making critical organisationnal decisions, particularly those of strategic nature. Cross-market analysis. useful in fraud detection and rare event analysis, Other pattern-directed or statistical analyses, Automated data collection tools and mature database technology lead to huge amounts of data accumulated. "description": "Automated data collection tools and mature database technology lead to huge amounts of data accumulated. to learn the necessary foundational skills in just 24 weeks. "contentUrl": "https://slideplayer.com/slide/5798869/19/images/7/Data+Mining+Functionalities.jpg", %PDF-1.4 Each of these techniques comprises an important aspect of data mining. The analysis of impromptu shopping behavior is an example of association that is, retailers notice in data studies that parents shopping for childcare supplies are more likely to purchase specialty food or beverage items for themselves during the same trip. Is concerned with the discovery of hidden knowledge. Shilpa Seth. 7 0 obj Government agencies use census data to map population trends and project spending needs while baseball teams use predictive models to determine contracts and build rosters. 95 percent of businesses say they have been impacted by poor data quality. endobj Introduction Data explosion problem: Automated data collection tools and mature database technology lead. stream endobj "@type": "ImageObject", Data warehouse. endobj Companies need. As a result, data science and visualization is a promising career path, and a data science and analytics bootcamp is a great way to learn the technical skills needed to solve complex data problems and visualize solutions. Ultimately, no matter how great their models or algorithms are, businesses suffer when their data is incorrect, incomplete, or corrupted. 16 0 obj bB !Z&!AM_%aD@+/I!VMYQ Q`Y\WF ojT7jUjh}kZnVhq3FSFf3ZT{vc@ShtRH&uL This is a primary way in which data scientists display their findings. "contentUrl": "https://slideplayer.com/slide/5798869/19/images/4/Data+Mining+Data+Mining+referred+using+a+number+of+names%3A.jpg", "@context": "http://schema.org", ", According to MicroStrategys 2018 Global State of Enterprise Analytics Report, 71 percent of global companies say they plan to spend more money on analytics (with 73 percent of U.S. companies intending to increase their analytics budgets). "@context": "http://schema.org", Statistical summary information (data central tendency and variation)", "name": "Data Mining Data Mining referred using a number of names:", Through data warehousing, businesses can segment and target customers from vast collections of sales orders, product searches, or loyalty program registrations. Multimedia database. This can help retailers target products and services to customers in a specific demographic or region. "@context": "http://schema.org", { Knowledge Discovery in Databases (1989): Also data archaeology, information harvesting, information discovery, knowledge extraction, data/pattern analysis, etc. MIning. /Group <> "contentUrl": "https://slideplayer.com/slide/5798869/19/images/11/Stages+of+KDD+Evaluation+%26+Presentation+Data+Mining.jpg", xWr6+6vFe-;'Cu&6I!r) JUoAFC=H{v}N? endobj Databases. Classification is also used to designate broad groups within a demographic, target audience, or user base through which businesses can gain stronger insights. "@type": "ImageObject", This is a growing need in many industries. Predict some unknown or missing numerical values. An increasingly relevant part of modern technology, machine learning makes computers smarter by teaching them how to perform tasks based on the data they have gathered. /Contents 6 0 R>> "name": "Fraud Detection & Mining Unusual Patterns", }, 9 Consider Georgia Tech Data Science and Analytics Boot Camp to learn the necessary foundational skills in just 24 weeks. Broadly speaking, data mining is the computer-driven process of exploring data sets, pinpointing key trends and anomalies, and subsequently analyzing these findings to form conclusions and make better decisions. Predict what factors will attract new customers. <> They also afford the opportunity to gain practical experience through real-world projects. 2022 SlidePlayer.com Inc. All rights reserved. "contentUrl": "https://slideplayer.com/slide/5798869/19/images/14/Market+Analysis+And+Management.jpg", endstream Parallel, distributed and incremental mining methods. KDD. It can also help car insurance companies that want to identify a set of customers who typically have high annual claims in order to price policies more effectively. Interactive mining of knowledge at multiple levels of abstraction. Almost every business can benefit from understanding anomalies in their production or distribution lines and how to fix them. Once data is classified, follow-up questions can be asked, and the results diagrammed into a chart called a decision tree. Data warehousing is the process by which data is collected and stored before it is evaluated. (gij "description": "Data analysis and decision support. ]p?r5IN`qTFtg%mGVy.*0}nwNhZqo#"LUMSRd )y ;*tL]f m.3i=.E+82 `FQyXDRD* Supervised and unsupervised learning also apply to neural networks; neural networks use these types of algorithms to train themselves to function in ways similar to the human brain. What Is Data Visualization in Data Mining? Is concerned with the discovery of hidden knowledge. ", Scatter plots can be used to compare unique variables such as a countrys life expectancy or the amount of money spent on healthcare annually. Classification and Prediction. Usually works on large volumes of data. { What are data mining techniques used for? These graphics are used to highlight specific word or phrase instances appearing in a body of text; the larger the words size in the cloud, the more frequent its use. This is a popular visualization technique that represents data through different colors and shading to indicate patterns and ranges in the data. "description": "Finance planning and asset evaluation. #nFc++Fkp4 )6y\L(uH^rK/KxmHNWM8$$CKEy#Zh{\sLp*_f}H]2>[O4B(oRjC!fI/E +6# U Money laundering: suspicious monetary transactions. Databases. ", Neurocomputing. , employment for computer and information research scientists is expected to climb by 15 percent through 2029. Meanwhile, predictive modeling is instrumental in developing more informed future plans based on existing findings. This involves dividing data into cells on a grid, which then can be clustered by individual cells rather than by the entire database. How much milk should a store have in stock on Monday? xWMs6Wc;Q%c_2ItI4Q&#$b`!E@fH"E }KzW{65/6zn?oOn7-6@uIZT> {&FKaEVQHxEkBFUQ 2B"I6kG k93#nK]Bj$RkdF@jx%huL stream Data Mining Data Mining referred using a number of names:Data Fishing, Data Dredging (1960): Used by statisticians (as bad name) Knowledge Discovery in Databases (1989): Used by AI, Machine Learning Community Business Intelligence (1990): Business management term Also data archaeology, information harvesting, information discovery, knowledge extraction, data/pattern analysis, etc. "width": "800" Q{8qbxY@u5M_!y#a`\x,LE`vNyq/0xwnQKr=!9G74[zN!& }, 13 B@!mtz[o_L'`XZEW( "contentUrl": "https://slideplayer.com/slide/5798869/19/images/2/What+is+Data+Mining%2FKDD.jpg", This is also known as extreme value analysis. > n +*KFB PNG Outliers are detected based on the Interquartile Range, or the middle 50 percent of values. endobj ]E9(,vYKIq+~>"`@W].pXnpsj x%NnuC3.+1+=uFi4 x na8k\!s6xq90!FUyS}:A pB.Lp/zz}b1WfnI,|]-V;$$7-7q._, u7[;\IwlSL|cFI9_jh4|t, =qX=ifjwPgpslG]frEj8)!::4sb ` \II Noise or exception No! ",#(7),01444'9=82. 17 0 obj }, 11 This process, called data warehousing, typically occurs before the data mining process. <>>>] "name": "Data Mining: On What Kinds Of Data", Machine learning and data mining fall under the umbrella of data science but arent interchangeable terms. Monitor competitors and market directions. 10 Data Mining. "@type": "ImageObject", $.' A Dream Realized: How Hillary Hines Launched Her UX/UI Career, Breaking the Glass Ceiling Early On: How to Empower Girls in STEM, Create an Impact With Your UX/UI Portfolio: A Step-by-Step Guide, The Complete Cybersecurity Career Guide for Veterans, 10 Key Data Mining Techniques and How Businesses Use Them. Potential ApplicationsData analysis and decision support Market analysis and management Risk analysis and management Fraud detection and detection of unusual patterns Other applications Text mining ( , documents) and Web mining Stream data mining DNA and bio-data analysis saravanan methodology cpb
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