《Knowledge Discovery in Databases: An Overview》， William J. Frawley, Gregory Piatetsky-Shapiro, and Christopher J. Matheus， AAAI ，1992 Abstract： After a decade of fundamental interdisciplinary research in machine learning, the spadework in this field has been done; the 1990s should see the widespread exploitation of knowledge discovery as an aid to assembling knowledge bases. The contributors to the AAAI Press book \emph{Knowledge Discovery in Databases} were excited at the potential benefits of this research. The editors hope that some of this excitement will communicate itself to AI Magazine readers of this article the goal of this article： This article presents an overview of the state of the art in research on knowledge discovery in databases. We analyze Knowledge Discovery and define it as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. We then compare and contrast database, machine learning, and other approaches to discovery in data. We present a framework for knowledge discovery and examine problems in dealing with large, noisy databases, the use of domain knowledge, the role of the user in the discovery process, discovery methods, and the form and uses of discovered knowledge. We also discuss application issues, including the variety of existing applications and propriety of discovery in social databases. We present criteria for selecting an application in a corporate environment. In conclusion, we argue that discovery in databases is both feasible and practical and outline directions for future research, which include better use of domain knowledge, efficient and incremental algorithms, interactive systems, and integration on multiple levels. 个人点评： 一篇老些的经典数据挖掘综述，个人认为本文两个入脚点：一是Machine Learning (Table 1,2)，二是文中 Figure 1 Knowledge Discovery in Databases Overview.pdf beamer_Knowledge_Discovery_Database_Overview.pdf beamer_Knowledge_Discovery_Database_Overview.tex
Content 1.Introduction 2.Test data 3.Research Methods 4.Results Analysis 5.Summary 1. Introduction We use MODIS data to inverse the temperature and its spatial distribution in China over the past 10 years. And use the meteorological data to reconstruct the winter temperature anomaly time series of the urban, suburban, countryside and meteorological stations neighborhood in the last 60 years. We find that ： (1) meteorological observatory's temperature records may be affected by urban heat island effect; (2)China temperature fluctuated in the last 60 years and the small fluctuated cycle is about 7 years; (3)On the decade-scale, the biggest decadal temperature increased is 1990s; Although the early 21st century's temperatures is still in the warm period, but it has emerged the downward trend. 2 The Data Used in temperature inversion China Winter Temperature is mainly Controlled by the Siberian cold air, so the temperatures change is more consistent. We can use winter temperature as the indicators of climate change. We use the 20，22，23，29，31，32 bands of MODIS from 2000-2010 to inverse the temperature. The Coordinate system is WGS84, and the spatial resolution is 0.05 degree. We also use Chinese 722 meteorological stations' temperature records 1951-2009 in the study. 3 Temperature inversion Method Flow chart of temperature inversion based on MODIS data Monthly average temperatures uniformity test and revise Using all weather stations' monthly data 1951-2009 to establish November, December, January and Winter average temperature series. We systematically analysis each month's temperature trend and the winter temperature trend, then revise the temperature records. Specific steps are as follows: （1）Converse the 722 weather stations' monthly average temperature records to monthly average temperature anomaly data 1951-2009. （2）Test the conversed temperature data. If the monthly temperature anomaly is larger than +5 ℃ or less than -5 ℃, we will analysis the credibility of the stations' temperature records and correct it. Reconstruct the winter temperature anomaly time series (1)Change Meteorological stations' temperature records into grid temperature data: We mainly use the DEM which spatial resolution is 0.050.05 and the temperature records 1951-2010. The main method is Kriging interpolation. (2)Use MODIS data to inverse the winter average temperature and its spatial distribution 2000-2009. (3) Correct the 1951-2009 temperature which obtained by interpolated the meteorological records based on the temperature spatial distribution matrix . Reconstruct the winter temperature anomaly time series Spatial distribution map of China winter temperature Winter temperature distribution map of 2000 year based on interpolation 4.1 Temperature records affected by Urban heat island effect We use MODIS data to inverse China winter temperature anomaly trends 2000-2009, and find that temperature records affected by Urban heat island effect. Winter temperature anomaly trend in China (3-point sliding average curve) from 2000 to 2010 4.2 Centennial Temperature Variation Analysis Greenland temperatures change chart and the temperature changes in China (Zhu Kezhen, 1973) 4.2 Centennial Temperature Variation Analysis (1) The first two thousand years in Chinese historical five thousand years, the annual average temperature was 2 degrees Celsius higher than now. (2) Cold period started in the first millennium BC (Late Yin, early Zhou Dynasty), four centuries AD (the Six Dynasties), one thousand two hundred years AD (Song Dynasty) and one thousand seven hundred years AD(the Ming and Qing Dynasty). The Han and Tang dynasty was relatively warmer. (3)In each 400 to 800 years, we can separate 50-100 years cycle as a small climate variation cycle, the temperature range form 1 ℃ to 0.5 ℃ . 4.3 Decadal Temperature Variation Analysis Apply the cosine of the grid's latitude as weight，Calculate 1951-2009 China decadal temperature anomaly trend（figure 6）,and find that compared to 1951-1980 average temperature: (1) 1950s and 1960s is the cold period; (2) 1970s -2009 is a warm period, 1990s is the warmest decade of the 20th century ，the maximum of 20th centurys interdecadal temperature range is 0.27 ℃； (3)Although the temperature of the last decade is still higher than the 1951-1980 average temperature, but the temperature has a downward trend. Fig6 Interdecadal temperature anomaly trend in Winter since 1950s in china 4.4 Annual temperature variation analysis Fig7 Chinese winter temperature anomaly change trend from 1951 to 2009 Fig8 Winter temperature anomaly trend of Chinese cities since 1950s 4.4 Annual temperature variation analysis Through comparative analysis two graphs we know that: (1)There is 30 years whose winter temperatures higher than the 1951-1980 average temperature has 30 years, and 29 years less than the average. (2) Chinese temperature fluctuated over the past 60 years and the small fluctuated cycle is about 7 years; (3) Through the regression analysis on the temperature anomaly, we know that in the last 60 years，China temperature rise about 0.2 ℃， city temperature rise about 0.3 ℃ ；City temperature is higher than countryside（4.05 ℃ ）； (4)Since 2000, the temperature variation amplitude decreased，and temperature has a downward trend. Summary (1) The Warming in the last 60 years is a normal phenomenon of climate change. (2) Human activities has some impact on the temperature in some region, such as the urban and suburban is significantly warmer than in countryside. China 60 years temperature variation
Data Mining资源大全 默认分类 2009-07-18 21:16:43 阅读 134 评论 0 字号： 大 中 小 订阅 Da ta Mining: What Is Da ta Mining ? http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm Da ta Mining - An Introduction http://databases.about.com/library/weekly/aa100700a.htm?iam=excite_1terms=da ta+mining Da ta Mining - An Introduction Student Notes http://www.pcc.qub.ac.uk/tec/courses/datamining/stu_notes/dm_book_1.html Da ta Mining Overview http://www.megaputer.com/dm/index.php3 Da ta Mining - Award Winning Software http://www.salford-systems.com/?source=goto Da ta Mining With MicroStrategy Best In Business Intelligence http://www.microstrategy.com/Software/Mining.asp?CID=1818dm Da ta Mining, Web Mining and Knowledge Discovery Directory http://www.kdnuggets.com/ Da ta Miners Home Page http://www.da ta-miners.com/ Da ta Mining and Knowledge Discovery Journal http://www.digimine.com/usama/datamine/ Da ta Mining and Knowledge Discovery Journal http://www.kluweronline.com/issn/1384-5810 Effective Da ta Mining Technology http://www.enablesoft.com/ Find Da ta Mining Solutions http://www.knowledgestorm.com/SearchServlet?ksAction=keyMapx=da ta+miningsite=Overture Da ta Mining Solutions - 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ACM Special Interest Group On Knowledge Discovery and Da ta Mining http://www.acm.org/sigkdd/ Da ta Mining News http://www.idagroup.com/ NCDM National Center For Da ta Mining http://www.ncdm.uic.edu/ Da ta Mining Benchmarking Association (DMBA) http://www.dmbenchmarking.com/ Da ta Mining In Molecular Biology http://industry.ebi.ac.uk/~brazma/dm.html Da ta Mining and Machine Learning http://www.cs.helsinki.fi/research/fdk/datamining/ NCBI Tools For Da ta Mining http://www.ncbi.nlm.nih.gov/Tools/ Guide Your Organization's Future With Da ta Mining http://www.spss.com/spssbi/applications/datamining/ URLs For Da ta Mining http://www.galaxy.gmu.edu/stats/syllabi/DMLIST.html Generate maximum return on da ta in minimum time with Clementine http://www.spss.com/spssbi/clementine/ ICDM'02 The 2002 IEEE International Conference On Da ta Mining http://kis.maebashi-it.ac.jp/icdm02/ DMI: Da ta Mining Institute http://www.cs.wisc.edu/dmi/ Da ta Mining On The Web http://www.webtechniques.com/archives/2000/01/greening/ Da ta Mining Lecture Notes http://www-db.stanford.edu/~ullman/mining/mining.html ITSC Da ta Mining Center http://datamining.itsc.uah.edu/ Imperial College Da ta Mining Research Group http://ruby.doc.ic.ac.uk/ Knowledge Discovery Da ta Mining Foundation http://www.kdd.org/ Untangling Text Da ta Mining http://www.sims.berkeley.edu/~hearst/papers/acl99/acl99-tdm.html Directory Of Da ta Warehouse, Da ta Mining and Decision Support Resources http://www.infogoal.com/dmc/dmcdwh.htm Da ta Mining Techniques http://www.statsoftinc.com/textbook/stdatmin.html Knowledge Discovery In Biology and Medicine http://bioinfo.weizmann.ac.il/cards/knowledge.html SAS Analytic Intelligence Da ta Text Mining http://www.sas.com/technologies/da ta_mining/ Analysis of Da ta Mining Algorithms http://userpages.umbc.edu/~kjoshi1/da ta-mine/proj_rpt.htm BIOKDD, 2001 Workshop On Da ta Mining In Bioinformatics http://www.cs.rpi.edu/~zaki/BIOKDD01/ Advances In Knowledge Discovery and Da ta Mining http://www.aaai.org/Press/Books/Fayyad/fayyad.html On line Program In Da ta Mining http://www.ccsu.edu/datamining/ Da ta Mining: Concepts Techniques (Book) 2000 http://www.cs.sfu.ca/~han/DM_Book.html Tutorial On High Performance Da ta Mining http://www-users.cs.umn.edu/~mjoshi/hpdmtut/ GMDH Group Method Of Da ta Handling http://www.gmdh.net/ The Serendip Da ta Mining Project http://www.bell-labs.com/project/serendip/ Da ta Mining Forum http://www.da ta-mining-forum.de/ Open Directory: Da ta Mining http://dmoz.org/Computers/Software/Databases/Da ta_Mining/ Da ta Warehouse Information Center - Da ta Mining http://www.dwinfocenter.org/datamine.html Da ta Mining Magazine http://www.mining.dk/ Da ta Mining Server http://dms.irb.hr/ NAG Da ta Mining Components to Create Critical Competitive Advantage http://www.nag.co.uk/numeric/DR/drdescription.asp Da ta Mining and Multidimensional Analysis http://www.ics.uci.edu/~eppstein/gina/datamine.html ADC's Da ta Mining Resources For Space Science http://adc.gsfc.nasa.gov/adc/adc_datamining.html Laboratory For Knowledge Discovery In Databases (KDD) http://www.kddresearch.org/Groups/Da ta-Mining/ NCSA Da ta, Mining and Visualization http://archive.ncsa.uiuc.edu/DMV/ CRoss Industry Standard Process For Da ta Mining http://www.crisp-dm.org/ International Workshop On Visual Da ta Mining http://www-staff.it.uts.edu.au/~simeon/vdm_pkdd2001/ Mathematic Challenges In Scientific Da ta Mining http://www.ipam.ucla.edu/programs/sdm2002/ Mining Customer Da ta http://www.db2mag.com/db_area/archives/1998/q3/98fsaar.shtml Constraint-Based Multidimensional Da ta Mining http://www-sal.cs.uiuc.edu/~hanj/pdf/computer99.pdf 什么是数据挖掘 http://www.seamlessit.com/documents/DataMiner/DM2002-05-24A.htm 数据挖掘-技术与应用 http://www.seamlessit.com/documents/DataMiner/DM2002-05-24B.htm 数据挖掘助竞争 http://www.cai.com.cn/suc_story/0426.htm 数据挖掘讨论组 http://www.dmgroup.org.cn/ 数据挖掘在CRM中的应用 http://www.chinabyte.com/20020726/1622396.shtml Open Miner 数据挖掘工具 http://www.neusoft.com/UploadFile/0.4.3/217/217.htm 数据挖掘-概念与技术（影印书） http://www.hep.edu.cn/books/computer/photocopy/20.html 数据挖掘在科学数据库中的应用探索 http://www.sdb.ac.cn/thesis/thesis5/paper/p6.doc 数据挖掘概述 (一) http://www.ccf-dbs.org.cn/pages_c/datamining1.htm 数据挖掘概述 (二) http://www.ccf-dbs.org.cn/pages_c/datamining2.htm 数据挖掘在CRM中的核心作用 http://www.cndata.com/sjyw/dcd_knowlege/texts/article491.asp 网络数据挖掘 http://www.pcworld.com.cn/2000/back_issues/2014/1436a.asp 构建面向CRM的数据挖掘应用 2001 人民邮电出版社 http://www.e-works.net.cn/business/category18/126700621324531250.html 数据挖掘在CRM中的应用 http://www.e-works.net.cn/ewkArticles/Category38/Article9809.htm 数据挖掘及其工具的使用 http://eii.dlrin.edu.cn/zjlw/zhlw17.htm 数据挖掘-极具发展前景的新领域 http://www.creawor.com/biforum/bi_02.htm 数据挖掘的研究现状 http://www.creawor.com/biforum/bi_03.htm 数据挖掘-数据库技术的新时代 http://www.china-pub.com/computers/emook/1188/info.htm XML 与面向Web的数据挖掘技术 http://www.aspcool.com/lanmu/browse1.asp?ID=719bbsuser=xml http://www.swm.com.cn/rj/2000-10/25.htm http://www.ccidnet.com/tech/web/2001/09/04/58_3176.html 上海市计算机学会数据挖掘技术讨论网站 http://scs.stc.sh.cn/main/sjwj.htm 数据挖掘与统计工作 http://www.bjstats.gov.cn/zwxx/wzxw/zzwz/200207020115.htm 数据仓库、数据集市和数据挖掘 http://eii.dlrin.edu.cn/zjlw/zhlw16.htm 数据挖掘-图书馆员应掌握的基本工具 http://www.zslib.com.cn/xhlw/wk.doc 数据挖掘技术概述 http://www.china-pub.com/computers/emook/0903/info.htm 数据挖掘及其在工程诊断中的应用(博士论文) http://www.monitoring.com.cn/papers/GaoYilong_C_D.htm 本文来自CSDN博客，转载请标明出处： http://blog.csdn.net/evane1890/archive/2007/12/19/1954152.aspx 本文引用地址： http://www.sciencetimes.com.cn/m/user_content.aspx?id=242094
A geometric triangulation has two aspects: 1, the combinatorial structure, which gives the incidence and adjacency relations between faces, 2, the geometric information related to the position of vertices 38.1 Representation incident: 一个高维的face 和一个低维的face 是incedent ， 意思 是指 低维的face 是 高维face 的了sub face adjacent: 两个同维的face是相邻的，意思是它们共享同一个incicent (sub) face. 数据结构可以表示退化的情形： 3D： 5个点， 一个无限点，四个有限点 2D：4个点，一个无限点，三个有限点 1D：3个点， 一个无限点，二个有限点 0D：2个点，一个无限点，一个有限点 －1D： 只有一个无限点。This dimension is a convention to represent a 0-dimensional simplex, that is a sole vertex －2D：没有点。This is also a convention. use the classes Triangulation_vertex_base_with_info and Triangulation_cell_base_with_info , which allow to add one data member of a user provided type, and give access to it. derive his own classes from the default base classes Triangulation_ds_vertex_base, and Triangulation_ ds_cell_base (or the geometric versions typically used by the geometric layer, Triangulation_vertex_base , and Triangulation_cell_base ). write his own base classes following the requirements given by the concepts TriangulationCellBase_3 and TriangulationVertexBase_3 (described in page 2494 and page 2495).
36.1 Definition 36.1.1 A Data Structure Based on Faces and Vertices The triangulation data structure can be seen as a container for faces and vertices maintaining incidence and adjacency relations among them. Each triangular face gives access to its three incident vertices and to its three adjacent faces. Each vertex gives access to one of its incident faces and through that face to the circular list of its incident faces. 36.1.2 The Set of Faces and Vertices 表示的可扩展性 36.2 The Concept of Triangulation Data Structure 容器的作用；三角剖分组合信息集成的作用 the triangulation data structure is required to provide: the types Vertex and Face for the vertices and faces of the triangulations the type Vertex handle and Face handle which are models of the concept Handle and through which the vertices and faces are accessed. iterators to visit all the vertices, edges and faces of the triangulation, circulators to visit all the vertices, edges and faces incident to a given vertex 36.3 The Default Triangulation Data Structure 36.3.1 Flexibility 36.3.2 The Cyclic Dependency of Template Parameters Since adjacency and incidence relation are stored in vertices and faces, the vertex and face classes have to know the types of handles on faces and vertices provided by the triangulation data structure. Therefore, vertex and face classes need to be templated by the triangulation data structure. Because the triangulation data structure is itself templated by the vertex and face classes this induces a cyclic dependency . 36.3.3 The Rebind Mechanism The vertex and face classes plugged in the instantiation of a triangulation data structure are themselves instantiated with a fake data structure Rebind_TDS 36.3.4 Making Use of the Flexibility
Rare Sharing of Data Leads to Progress on Alzheimers By GINA KOLATA Published: August 12, 2010 In 2003, a group of scientists and executives from the National Institutes of Health, the Food and Drug Administration, the drug and medical-imaging industries, universities and nonprofit groups joined in a project that experts say had no precedent: a collaborative effort to find the biological markers that show the progression of Alzheimers disease in the human brain. Now, the effort is bearing fruit with a wealth of recent scientific papers on the early diagnosis of Alzheimers using methods like PET scans and tests of spinal fluid. More than 100 studies are under way to test drugs that might slow or stop the disease. And the collaboration is already serving as a model for similar efforts against Parkinsons disease. A $40 million project to look for biomarkers for Parkinsons, sponsored by the Michael J. Fox Foundation, plans to enroll 600 study subjects in the United States and Europe. The work on Alzheimers is the precedent, said Holly Barkhymer, a spokeswoman for the foundation. Were really excited. The key to the Alzheimers project was an agreement as ambitious as its goal: not just to raise money, not just to do research on a vast scale, but also to share all the data, making every single finding public immediately, available to anyone with a computer anywhere in the world. No one would own the data. No one could submit patent applications, though private companies would ultimately profit from any drugs or imaging tests developed as a result of the effort. It was unbelievable, said Dr. John Q. Trojanowski, an Alzheimers researcher at the University of Pennsylvania. Its not science the way most of us have practiced it in our careers. But we all realized that we would never get biomarkers unless all of us parked our egos and intellectual-property noses outside the door and agreed that all of our data would be public immediately. Biomarkers are not necessarily definitive. It remains to be seen how many people who have them actually get the disease. But that is part of the research project. The idea for the collaboration, known as ADNI, for Alzheimers Disease Neuroimaging Initiative, emerged about 10 years ago during a casual conversation in a car. Neil S. Buckholtz, chief of the Dementias of Aging Branch at the National Institute on Aging, was in Indianapolis, and Dr. William Potter, a neuroscientist at Eli Lilly and his longtime friend, was driving him to the airport. Dr. Potter had recently left the National Institutes of Health and he had been thinking about how to speed the glacial progress of Alzheimers drug research. We wanted to get out of what I called 19th-century drug development give a drug and hope it does something, Dr. Potter recalled in an interview on Thursday. What was needed was to find some way of seeing what was happening in the brain as Alzheimers progressed and asking if experimental drugs could alter that progression. Scientists were looking for biomarkers, but they were not getting very far. The problem in the field was that you had many different scientists in many different universities doing their own research with their own patients and with their own methods, said Dr. Michael W. Weiner of the San Francisco Department of Veterans Affairs, who directs ADNI. Different people using different methods on different subjects in different places were getting different results, which is not surprising. What was needed was to get everyone together and to get a common data set. But that would require a huge effort. No company could do it alone, and neither could individual researchers. The project would require 800 subjects, some with normal memories, some with memory impairment, some with Alzheimers, who would be tested for possible biomarkers and followed for years to see whether these markers signaled the diseases progression. Suddenly, in the car as he drove Dr. Buckholtz to the airport, everything just jelled, Dr. Potter said, adding, Maybe this was important enough to get people to work together and coordinate in a way that hadnt been possible before. The idea, Dr. Buckholtz said, was that the governments National Institutes of Health could serve as an honest broker between the pharmaceutical industry and academia. Soon, Dr. Richard J. Hodes, the director of the National Institute on Aging, was on the phone with Dr. Steven M. Paul, a former scientific director at the National Institute of Mental Health who had recently left to head central-nervous-system research at Eli Lilly. Dr. Paul offered to ask other drug companies to raise money. It turned out to be relatively easy to get companies to agree, Dr. Paul said. It had become clear that the problem of finding good diagnostic tools was huge and complex. We were better off working together than individually, he said. A critical aspect of the project was the Foundation for the National Institutes of Health, which was set up by Congress to raise private funds on behalf of the institutes. Dr. Paul was on its board. In the end, the National Institute on Aging agreed to pay $41 million, other institutes contributed $2.4 million, and 20 companies and two nonprofit groups contributed an additional $27 million to get the project going and sustain it for the first six years. Late last year, the institute contributed an additional $24 million and the foundation was working on a renewal of the project for another five years that would involve federal and private contributions of the same magnitude as the initial ones. At first, the collaboration struck many scientists as worrisome they would be giving up ownership of data, and anyone could use it, publish papers, maybe even misinterpret it and publish information that was wrong. But Alzheimers researchers and drug companies realized they had little choice. Companies were caught in a prisoners dilemma, said Dr. Jason Karlawish, an Alzheimers researcher at the University of Pennsylvania. They all wanted to move the field forward, but no one wanted to take the risks of doing it. Many people look askance at collaborations with drug companies, and often that attitude is justified, Dr. Karlawish said. But not in this case. To those who are skeptical, he says, My answer to them is get over it. He went on: This one makes sense. The development of reliable and valid measures of Alzheimers disease requires such large science with such limited returns on the investment that it was in no one companys interest to pursue it. Companies as well as academic researchers are using the data. There have been more than 3,200 downloads of the entire massive data set and almost a million downloads of the data sets containing images from brain scans. And Dr. Buckholtz says he is pleasantly surprised by the way things are turning out. We werent sure, frankly, how it would work out having data available to everyone, he said. But we felt that the good that could come out of it was overwhelming. And thats whats happened. http://www.nytimes.com/2010/08/13/health/research/13alzheimer.html?pagewanted=1hp
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