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Data Mining: Concepts and Techniques

Data mining : Concepts and TechniquesData mining : Concepts and TechniquesSecond EditionJiawei HanandMicheline KamberUniversity of Illinois at Urbana-ChampaignA M S T E R D A M B O S T O NH E I D E L B E R G L O N D O NN E W Y O R K O X F O R D P A R I SS A N D I E G O S A N F R A N C I S C OS I N G A P O R E S Y D N E Y T O K Y OPublisherDiane CerraPublishingServices Manager Simon CrumpEditorial AssistantAsma StephanCover DesignCover ImageCover IllustrationText DesignCompositiondiacriTechTechnical IllustrationDartmouth Publishing, PressProofreaderMultiscience PressIndexerMultiscience PressInterior printerMaple-Vail Book Manufacturing GroupCover printerPhoenix ColorMorgan Kaufmann Publishers is an imprint of Sansome Street.

5.2.5 Mining Frequent Itemsets Using Vertical Data Format 245 5.2.6 Mining Closed Frequent Itemsets 248 5.3 Mining Various Kinds of Association Rules 250 5.3.1 Mining Multilevel Association Rules 250 5.3.2 Mining Multidimensional Association Rules from Relational Databases and Data Warehouses 254 5.4 From Association Mining to Correlation ...

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Transcription of Data Mining: Concepts and Techniques

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Data Mining: Concepts and Techniques

hanj.cs.illinois.edu

3.5 From Data Warehousing to Data Mining 146 3.5.1 Data Warehouse Usage 146 3.5.2 From On-Line Analytical Processing to On-Line Analytical Mining 148 3.6 Summary 150 Exercises 152 Bibliographic Notes 154 Chapter 4 Data Cube Computation and Data Generalization 157 4.1 Efficient Methods for Data Cube Computation 157

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2.4.8 Kullback-Leibler Divergence

hanj.cs.illinois.edu

from an observed frequency distribution, as illustrate in the following example. Example 2.24. Computing the KL Divergence by Smoothing. Sup-pose there are two sample distributions P and Q as follows: P: (a: 3/5,b:

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Web Mining — Concepts, Applications, and Research …

dmr.cs.umn.edu

Mining efforts here have focused on automatically extracting document object model (DOM) structures out of documents (Wang and Liu 1998; Moh, Lim, and Ng 2000). 21.1.3 Web Usage Mining Web usage mining is the application of data mining techniques to discover interesting usage patterns from web usage data, in order to understand and

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Data Mining Classification: Basic Concepts and Techniques

www-users.cse.umn.edu

Classification Model 2/1/2021 Introduction to Data Mining, 2nd Edition 4 3 4. Classification Techniques

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Safety management systems in mines

www.resourcesandenergy.nsw.gov.au

Western Australia’ and forms part of the mining safety legislative framework for these states. Under this agreement, tri-state model legislation was developed that is to be structured and customised differently in each of these states. This code was also developed in consultation with the Non-Core (tri-state) Legislative Working ...

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Anchors: High Precision Model-Agnostic Explanations

homes.cs.washington.edu

Given a black box model f: X!Yand an instance x2X, the goal of local model-agnostic interpretability (Ribeiro, Singh, and Guestrin 2016a; 2016b; Strumbelj and Kononenko 2010) is to explain the behavior of f(x) to a user, where f(x) is the individual prediction for instance x. The assumption is that while the model is globally too complex to be ...

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What is Cluster Analysis?

www.stat.columbia.edu

• Model-based: A model is hypothesized for each of the clusters and the idea is to find the best fit of that model to each other. Partitioning Algorithms: Basic Concept • Partitioning method: Construct a partition of a database D of n objects into a set of k clusters

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model into a trustworthy one { for example, removing leaked data or changing the training data to avoid dataset shift. Machine learning practitioners often have to select a model from a number of alternatives, requiring them to assess the relative trust between two or more models. In Figure Figure 2: Explaining individual predictions of com-

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node2vec: Scalable Feature Learning for Networks

cs.stanford.edu

eralizes prior work and can model the full spectrum of equivalences observed in networks. The parameters governing our search strat-egy have an intuitive interpretation and bias the walk towards dif-ferent network exploration strategies. These parameters can also be learned directly using a tiny fraction of labeled data in a semi-supervised ...

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Mining of Massive Datasets - Stanford University

infolab.stanford.edu

also introduced a large-scale data-mining project course, CS341. The book now contains material taught in all three courses. What the Book Is About At the highest level of description, this book is about data mining. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory.

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