CS699 Lecture 10 Clustering What is Cluster Analysis? Cluster: A collection of data objects similar (or related) to one another within the same group dissimilar (or unrelated) to the objects in other groups Cluster analysis (or clustering, data segmentation, …) Finding similarities between data according to the characteristics found in […]
CS699 Lecture 2 Data Exploration Types of Data Sets – Data matrix, e.g., numerical matrix, crosstabs • Record – Relational records – Document data: text documents: term‐ frequency vector – Transaction data • Graph and network – World Wide Web – Social or information networks – Molecular Structures • Ordered – Video data: sequence of
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Link Analysis Algorithms Page Rank With slide contributions from J. Leskovec, Anand Rajaraman, Jeffrey D. Ullman, Wensheng Wu The Problem Web as a Graph • Web as a directed graph: • Nodes: Webpages • Edges: Hyperlinks CS224W: Classes are in the Gates building Computer Science Department at Stanford I teach a class on Networks. Stanford
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Naïve Bayes Classification AI lecture: Machine Learning Naïve Bayes Classification — Basic Machine Learning Model Material borrowed (and modified) from Jonathan Huang and I. H. Witten’s and E. Frank’s “Data Mining” and Jeremy Wyatt and others and revised by C.C. Hung * Outline Probability and Machine Learning Bayesian Classification Naïve Bayesian Classifier Examples Model parameters
NUMERICAL MATHEMATICS AND COMPUTING Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience.
Lecture Notes to Accompany Scientific Computing An Introductory Survey Second Edition by Michael T. Heath Chapter 3 Linear Least Squares Copyright ⃝c 2001. Reproduction permitted only for noncommercial, educational use in conjunction with the book. 1 Method of Least Squares Measurement errors inevitable in observational and experimental sciences Errors smoothed out by averaging over many
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High dim. data Dimension ality reduction Graph Infinite data data PageRank, Machine learning Apps J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org 2 Facebook social graph 4-degrees of separation [Backstrom-Boldi-Rosa-Ugander-Vigna, 2011] J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, http://www.mmds.org 3 Connections between political blogs Polarization of the network [Adamic-Glance,
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////////////////////////////////////////////////////////////////////// // // File: Token.java // Project: MIE457F Information Retrieval System // Author: Scott Sanner, University of Toronto (ssanner@cs.toronto.edu) // Date: 9/1/2003 // ////////////////////////////////////////////////////////////////////// // Package definition package ppddl; // Packages to import import java.io.*; import java.math.*; import java.util.*; /** * A generic token structure for TokenStream * * @version 1.0 * @author Scott Sanner
////////////////////////////////////////////////////////////////////// // // File: TokenStreamException.java // Project: MIE457F Information Retrieval System // Author: Scott Sanner, University of Toronto (ssanner@cs.toronto.edu) // Date: 9/1/2003 // ////////////////////////////////////////////////////////////////////// // Package definition package ppddl; // Packages to import import java.lang.*; /** * An exception thrown by TokenStream * * @version 1.0 * @author Scott Sanner * @language Java (JDK 1.3)
CE306/CE706 – Information Retrieval Assignment 2 Alba García Seco de Herrera March 2021 Plagiarism 提醒您，此作品应归功于CE306 / CE706中的复合商标，因此，您提交的作品必须属于您自己的作品。您使用的任何材料，无论是教科书，Web还是任何其他来源的材料，都必须在程序中作为注释加以确认，并注明参考范围。 The context of your task 为了正确评估系统，您的测试信息需求必须与测试文档集中的文档密切相关（并与之相关），并且适合于系统的预期使用。给定信息需求和文档，您需要收集相关性评估。这是一个涉及人类（在本例中为您）的耗时且昂贵的过程。对于很小的集合，可以获得与每个查询和文档对相关的详尽判断。对于大型的现代馆藏，通常仅针对每个查询的一部分文档评估相关性。最标准的方法是合并，在其中评估集合的子集的相关性，该集合是由许多不同的IR系统（通常是要评估的文档）返回的前k个文档构成的。 The Document Collection (dataset) 对于此分配，您将使用在第一次分配中使用的数据集（分别用于CE306和CE706的Wikipedia电影情节或COVID-19开放研究数据集）。 Your task 此任务分阶段进行。每个阶段都有分数。阶段如下： • Building a Test Collection (10%)想象一下，您想探索哪种搜索引擎设置最适合您要建立索引的集合，从而使搜索尽可能高效。首先，您应该设计一个小的测试集合，其中包含许多查询及其预期结果。 o确定集合涵盖的三个信息需求，然后为每个需求组成一个样本查询。 • IR systems (20%)您将比较2个IR系统。在第一个任务中，您构建了一个IR系统，即系统1。对于系统2，然后可以更改不同的参数。例如，您可以通过比较使用词干的系统和不使用词干的系统来更改预处理管道。但是，这将要求您重新索引集合。另外，您可能想尝试不同的检索模型，例如布尔值与TF.IDF。 • Pooling (10%)您将把两个IR系统（来自作业1的原始结果和新创建的一个）的前10个检索结果汇总在一起来构建池。您需要对三个查询中的每个查询都执行此操作。在下一步中，您将判断此池中的每个文档。 • N.B. 池外的文档自动被认为是不相关的（Sparck Jones和van Rijsbergen，1975） • Assessing relevance (20%)您将提供二进制相关性判断。文档与信息需求相关或无关（无关）。 o对于每个信息需求对（查询），您需要评估池中的每个文档是否相关（如果它满足信息需求）。
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