$textbf{Trajectory Data Mining: An Overview}$ 很好的一篇概述,清晰明了地阐述了其框架,涉及内容又十分宽泛.值得细读. 未完成,需要补充. $textbf{Trajectory Data}$:主要分为四个类别 $texttt{Mobility of people}$ $texttt{Mobility of transportation}$ $texttt{Mobility of animals}$ $texttt{Mobility of natural…

Course textbooks Text 1: M. T. Oszu and P. Valduriez, Principles of Distributed Database Systems, 2nd ed., Prentice-Hall, 1999.Errata Text 2: J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.Errata Lecture Schedule Th…

What is the most common software of data mining? 1 Orange? 2 Weka? 3 Apache mahout? 4 Rapidminer? 5 R? and which one? If you have any explanation about the topic, I appreciate it.…

Data mining is the process of finding patterns in a given data set. These patterns can often provide meaningful and insightful data to whoever is interested in that data. Data mining is used today in a wide variety of contexts – in fraud detection, a…

Abstract The content of the web has increasingly become a focus for academic research. Computer programs are needed in order to conduct any large-scale processing of web pages, requiring the use of a web crawler at some stage in order to fetch the pa…

https://github.com/mattbane/RecommenderSystem http://grouplens.org/datasets/movielens/ KDDCUP-2012官网 From kdnuggets Data repositories AWS (Amazon Web Services) Public Data Sets, provides a centralized repository of public data sets that can be seamle…

https://en.wikipedia.org/wiki/K-means_clustering k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k…

官方网站: Weka 3: Data Mining Software in Java 相关使用方法博客 WEKA使用教程(经典教程转载) (实例数据:bank-data.csv) Weka初步一.二.三.四 使用Weka进行数据挖掘 一个小时速度入门数据挖掘WEKA(一个完整的小例子) 百度文库 WEKA中文详细教程(全) WEKA 3-5-3 Experimenter 指南 数据挖掘工具(weka教程)   基本概念 classify分类     cluster聚类     Associate…

数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)的区别是什么? 数据科学(data science)和商业分析(business analytics)之间有什么关系? 本来我以为不需要解释这个问题的,到底数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)有什么区别,但是前几天因为有个学弟问我,我想了想发现我竟然也回答不出来,我在知乎和博客上查了查这个问题,发现还没有人写过比较详细和有说服力的对比…

本来我以为不需要解释这个问题的,到底数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)有什么区别,但是前几天因为有个学弟问我,我想了想发现我竟然也回答不出来,我在知乎和博客上查了查这个问题,发现还没有人写过比较详细和有说服力的对比和解释.那我根据以前读的书和论文,还有和与导师之间的交流,尝试着说一说这几者的区别吧,毕竟一个好的定义在未来的学习和交流中能够发挥很大的作用.同时补上数据科学和商业分析之间的关系.能力有限,如有疏漏,请包涵和指正. 导论…

https://www.youtube.com/user/WekaMOOC 大学公开课  视频教程 weka 入门教程 data Mining with Weka: Trailer  More Data Mining with Weka data Mining with Weka: Trailer  More Data Mining with Weka 用weka 进行数据挖掘 用weka 进行更多数据挖掘 https://www.youtube.com/watch?v=LcHw2ph6bss&…

Machine Learning and Data Mining Lecture 1 1. The learning problem - Outline     1.1 Example of machine learning Predicting how a viewer will rate a moive? 10% improvement = 1 million dollar prize The essence of machine learning: A pattern exists We…

Conference WSDM(Web Search and Data Mining)The ACM WSDM Conference Series 不像KDD.WWW或者SIGIR,WSDM因为从最开始就由不少工业界的学术领导人发起并且长期引领,所以十分重视工业界的学术成果的展现. 2017 WSDM 2017精选论文解读 2016 前沿理论.反思创新.产学结合--你不能错过的WSDM 2016大会 Accepted Paper 2015 严谨与特色并行--WSDM 2015大会见闻记 其他 聚…

How do you explain Machine Learning and Data Mining to non Computer Science people?   Pararth Shah, ML Enthusiast Answered Dec 22, 2012 · Featured on VentureBeat · Upvoted by Melissa Dalis, CS & Math major at Duke and Alberto Bietti, PhD student in m…

Data Mining的十种分析方法: 记忆基础推理法(Memory-Based Reasoning:MBR)        记忆基础推理法最主要的概念是用已知的案例(case)来预测未来案例的一些属性(attribute),通常找寻最相似的案例来做比较.        记 忆基础推理法中有两个主要的要素,分别为距离函数(distance function)与结合函数(combination function).距离函数的用意在找出最相似的案例:结合函数则将相似案例的属性结合起来,以供预测之用.…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…

前言:工欲善其事,必先利其器.倘若不懂得构建一套大数据挖掘环境,何来谈Data Mining!何来领悟“Data Mining Engineer”中的工程二字!也仅仅是在做数据分析相关的事罢了!此文来自于笔者在实践项目开发中的记录,真心希望日后成为所有进入大数据领域挖掘工程师们的良心参考资料.下面是它的一些说明: 它是部署在Windows环境,在项目的实践开发过程中,你将通过它去完成与集群的交互,测试和发布: 你可以部署成使用MapReduce框架,而本文主要优先采用Spark版本: 于你而言,…

Learning Resources 书籍: 期刊: 业界先驱: 开阔视野,掌握业界最新动态. 工具: 数据挖掘是很多学科的综合体: 甭管叫什么名字,归根到底都是数据挖掘: Comprehensive Learning: Learning != Listening 数据 What is Big Data? Big Data: Data Mning Data Integration & Analasis The Process of Data Mining DM Techniques -- Cla…

题目传送门 /* 题意:从i开始,之前出现过的就是之前的值,否则递增,问第p个数字是多少 莫队算法:先把a[i+p-1]等效到最前方没有它的a[j],问题转变为求[l, r]上不重复数字有几个,裸莫队:) */ #include <cstdio> #include <algorithm> #include <map> #include <set> #include <vector> #include <cmath> #include…

原文: Wu X, Zhu X, Wu G Q, et al. Data mining with big data[J]. IEEE transactions on knowledge and data engineering, 2013, 26(1): 97-107. 大数据中的数据挖掘 Xindong Wu, Fellow, IEEE, Xingquan Zhu, Senior Member, IEEE, Gong-Qing Wu, and Wei Ding, Senior Member,…

Classification============== #1. C4.5 Quinlan, J. R. 1993. C4.5: Programs for Machine Learning.Morgan Kaufmann Publishers Inc. Google Scholar Count in October 2006: 6907 #2. CART L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification andReg…

做Data Mining,其实大部分时间都花在清洗数据 时间 2016-12-12 18:45:50  51CTO 原文  http://bigdata.51cto.com/art/201612/524771.htm 主题 数据挖掘 前言:很多初学的朋友对大数据挖掘第一直观的印象,都只是业务模型,以及组成模型背后的各种算法原理.往往忽视了整个业务场景建模过程中,看似最普通,却又最精髓的特征数据清洗.可谓是平平无奇,却又一掌定乾坤,稍有闪失,足以功亏一篑. 大数据圈里的一位扫地僧 说明:这篇文章很…

from here 论文Timeseries data mining(2012)中提出:时间序列数据挖掘包括7个基本任务和3个基础问题: 7 tasks: query by content clustering classification segmentation?? prediction anomaly detection motif discovery 3 Issues: data representation similarity measure indexing 现已有2013-201…

Chapter 1 data mining is knowledge discovery from data; The knowledge discovery process is an iterative sequence of 7 steps: data cleaning: to remove noise and inconsistent data data integration: where multiple data sources may be combined (step1 and…

data ------> knowledge Are all patterns interesting? No. only a small fraction of the patterns potentially generated would actually be of interest to a given user. What makes a pattern interesting? easily understood by humans valid potentially useful…

机器学习常见算法分类汇总 | 码农网 数据挖掘十大经典算法 | CSDN博客 (内含十个算法具体介绍) 支持向量机通俗导论(理解 SVM 的三层境界)| CSDN博客 (强烈推荐关注博主) 教你如何迅速秒杀掉:99% 的海量数据处理面试题 | CSDN博客 从 B 树.B+树.B* 树谈到 R 树 | CSDN博客 从头到尾彻底理解 KMP(2014年8月22日版) | CSDN博客 LinkedIn 开源的机器学习工具包:1 & 2 | 支持单机.Hadoop cluster 和 Spark…

Implicit rating and item based filtering Explicit rating: 用户明确的对item评分 Implicit rating:反之 明确评分所存在的问题: 1. 用户懒惰,不评分 2.用户可能撒谎或者只给出部分信息 3. 用户不会在更新他们的评分无论感觉产品是好还是差 不明确评分所存在的问题: 1.为自己的朋友或亲人购买礼物 2.两个人(couple)共用同一个用户名浏览网站或购买东西 Implicit data:(仅仅浅浅列出一些例子) 网页内容…

Here is the note for lecture three. the linear model Linear model is a basic and important model in machine learning. 1. input representation     The data we get usually needs some changes, most of them is the input data.      In linear model,       …

Here is the note for lecture five. There will be several points  1. Training and Testing  Both of these are about data. Training is using the data to get a fine hypothesis, and testing is not. If we get a final hypothesis and want to test it, it turn…

Course descriptionWith the continuing advances of geographic information science and geospatialtechnologies, spatially referenced information have been easily and increasinglyavailable in the past decades and becoming important information sources in…