7. In Hunt's algorithm, a decision tree is grown in a recursive fashion by partitioning the training records into successively purer subsets. Let Dl be the set of training records that are associated with node t and g : {At,U2, . . .,A"} be the class labels. • Hunt's algorithm Step 1 : If ail the records in Dt belong to the same class y1, then t is a leaf node labeled as y.
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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar. Why Mine Data? Commercial Viewpoint • Lots of data is being collected and warehoused • …
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Updated Slides for CS, UIUC Teaching in PowerPoint form (Note: This set of slides corresponds to the current teaching of the data mining course at CS, UIUC. In general, it takes new …
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11. 11 How to Handle Noisy Data? Binning first sort data and partition into (equal-frequency) bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. Regression smooth by fitting the data into regression functions Clustering detect and remove outliers Combined computer and human inspection detect suspicious values and …
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18. 17-Dec-19 Customer Profiling Identified as customer-oriented strategy in e-commerce Use business intelligence through the mining of customer's data Plan their business activities and operations Develop new research on products or services for prosperous e-commerce Categorizing the customers of great purchasing potentially from the visiting data The most of …
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This document discusses data mining and different types of data mining techniques. It defines data mining as the process of analyzing large amounts of data to discover patterns and relationships. The document …
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On Knowledge and Data Eng. (TKDE) • KDD Explorations • ACM Trans. on KDD • KDD Conferences • ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD) • SIAM Data Mining Conf. (SDM) • (IEEE) Int. Conf. on Data Mining (ICDM) • European Conf. on Machine Learning and Principles and practices of Knowledge ...
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4. Classification Classification is a most familiar and most popular data mining technique. Classification applications includes image and pattern recognition, loan approval, detecting faults in industrial applications. All approaches to performing classification assumes some knowledge of the data. Training set is used to develop specific parameters required by …
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29. DBMS, OLAP, and Data Mining DBMS OLAP Data Mining Task Extraction of detailed and summary data Summaries, trends and forecasts Knowledge discovery of hidden patterns and insights Type of result Information Analysis Insight and Prediction Method Deduction (Ask the question, verify with data) Multidimensional data modeling, Aggregation, Statistics …
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Data mining combines domain experience, computer abilities, and mathematical and statistical understanding to extract valuable insights from data. Here is a professionally designed Data …
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Template 3: Data Mining PPT Template Bundles. This Data Mining PPT Template Bundle is the perfect way to make your data mining presentations stand out. The template covers topics such as IT data mining process phases, challenges and solutions, data mining techniques, applications, data analysis, and more.
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Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. It supplements the discussions in the other chapters with a discussion of the statistical concepts (statistical significance, p-values, false discovery …
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers …
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10. Grid-Based methods in clusteringSTING: Statistical information gridSTING is a grid-based multi resolution clustering technique in which the spatial area is divided into rectangular cells.Wave Cluster: Clustering Using Wavelet TransformationWave Cluster is a multi resolution clustering algorithm that first summarizes the data by imposing a multidimensional …
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4. 4 Introduction • Spatial data mining is the process of discovering interesting, useful, non-trivial patterns from large spatial datasets – E.g. Determining hotspots: unusual locations. • Spatial Data Mining Tasks – Characteristics rule. – Discriminate rule. E.g. Comparison of price ranges of different geographical area.
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15 Multi-Dimensional View of Data Mining Data to be mined Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks Knowledge to be mined (or: Data mining functions) Characterization, discrimination, …
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Whether it's improving customer satisfaction, mitigating risks, or optimizing marketing strategies, data mining plays a crucial role in driving success and achieving business goals across different sectors. Download …
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CS145: INTRODUCTION TO DATA MINING Instructor: Yizhou Sun. [email protected] September 23, 2021. 1: Introduction
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4. Clustering • Clustering means grouping the objects based on the information found in the data describing the objects or their relationships. • The goal is that the objects in a group will be similar (or related) to one other and different from (or unrelated to) the objects in other groups. • grouped according to logical relationships or consumer preferences. • …
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3 What is Data Mining By definition is the process of extracting previously unknown data from large databases and using it to make orgnisational decisions. Is concerned with the discovery of hidden knowledge. Usually works on large volumes of data Is useful in making critical organisationnal decisions, particularly those of strategic nature
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8. Classification—A Two-Step Process • Model usage: for classifying future or unknown objects – Estimate accuracy of the model • The known label of test samples is compared with the classified result from the model • Accuracy rate is the percentage of test set samples that are correctly classified by the model • Test set is independent of training set, …
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The Object Data Standard Edited by R. G. G. Cattell, Douglas Barry Data on the Web: From Relations to Semistructured Data and XML Serge Abiteboul, Peter Buneman, Dan Suciu Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 3rd Edition Ian Witten, Eibe Frank, Mark A. Hall
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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 3 …
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Alternative names Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business …
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Data mining basic fundamentals - Download as a PDF or view online for free ... • Download as PPT, PDF ... Data warehouse • Centralized storage • Free use of data 09/06/18 33 34. How does data mining work? • …
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What Is Data Mining? • Data mining (knowledge discovery from data) • Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from …
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4. Data Mining: What is Data Mining? Data Mining means to mine data to extract useful information from it. Data mining is a process used by companies to turn raw data into useful information. (Data are any facts, numbers, or text that can be processed by a computer or any information that can be converted into knowledge about future trends.) Information that …
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Types of Data in Cluster Analysis. A Categorization of Major Clustering Methods ... Cluster Weblog data to discover groups of similar access patterns ... – A free PowerPoint PPT presentation (displayed as an HTML5 slide show) on PowerShow - id: c308d-ZDc1Z
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