Sat, 26 Jul 2008 04:30:51 BST CiteULike: Jaykul's library [44 articles] http://www.citeulike.org/user/Jaykul CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/Jaykul/article/1972782 <i>(2002)</i><br /><br />The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data. With our novel Growing Hierarchical SelfOrganizing Map presented in this paper we address... The growing hierarchical self-organizing maps: exploratory analysis of high-dimensional data A Rauber D Merkl M Dittenbach (2002) 2007-11-24T15:05:22-00:00 2002 ghsom som http://www.citeulike.org/user/Jaykul/article/889108 <i>Intelligent Vehicles '95 Symposium., Proceedings of the (1995), pp. 229-234.</i><br /><br />This paper describes a method for the interpretation of traffic scenes based on the detection and recognition of those objects, or classes of objects which are typically found in an urban scene. Since generic model-based recognition schemes are unsuitable for the analysis of traffic scenes and result in very poor performances, each of the different classes of objects which we expect to find in a typical scene is identified according to some selected features. After identifying the object, its main parameters are computed and, when needed, the object is further classified. The classes of objects we have considered included the roadbed, vehicles, buildings, trees, crosswalks and road signs. The method described here has been successfully tested on a wide set of images of traffic scenes and provided a general-purpose reconstruction of the whole traffic scene as viewed by the driver A feature-based recognition scheme for traffic scenes P Parodi G Piccioli Intelligent Vehicles '95 Symposium., Proceedings of the (1995), pp. 229-234. 2006-10-08T01:47:26-00:00 1995 Intelligent Vehicles '95 Symposium., Proceedings of the 229 234 computervision roadsign traffic http://www.citeulike.org/user/Jaykul/article/770142 <i>Intelligent Vehicles '94 Symposium, Proceedings of the (1994), pp. 278-283.</i><br /><br />This paper describes a method for detecting and recognizing road signs in grey-level images acquired by a single camera mounted on a moving vehicle. An extensive experimentation has shown that the method is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways. A further improvement on the detection and recognition scheme has been obtained by means of a Kalman-filter-based temporal integration of the extracted information. The proposed approach can be very helpful for the development of a system for driving assistance. Robust road sign detection and recognition from image sequences G Piccioli E De Micheli P Parodi M Campani doi:10.1109/IVS.1994.639527 Intelligent Vehicles '94 Symposium, Proceedings of the (1994), pp. 278-283. 2006-07-23T21:56:14-00:00 1994 Intelligent Vehicles '94 Symposium, Proceedings of the 278 283 computervision roadsign http://www.citeulike.org/user/Jaykul/article/305755 <i>Journal Of Information Science, Vol. 32, No. 2. (18 Aug 2005), pp. 198-208.</i><br /><br />Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge. The Structure of Collaborative Tagging Systems Scott Golder Bernardo Huberman doi:10.1177/0165551506062337 Journal Of Information Science, Vol. 32, No. 2. (18 Aug 2005), pp. 198-208. 2005-08-27T17:06:09-00:00 2005 Journal Of Information Science 32 2 198 208 Sage Publications, Inc. bookmarking collaborative social tagging http://www.citeulike.org/user/Jaykul/article/241012 <i>pp. 43-52.</i><br /><br />Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation... Empirical Analysis of Predictive Algorithms for Collaborative Filtering John Breese David Heckerman Carl Kadie pp. 43-52. 2005-06-30T15:48:40-00:00 43 52 algorithm collaborative filtering http://www.citeulike.org/user/Jaykul/article/171292 <i>(1994), pp. 175-186.</i> GroupLens: an open architecture for collaborative filtering of netnews Paul Resnick Neophytos Iacovou Mitesh Suchak Peter Bergstrom John Riedl doi:10.1145/192844.192905 (1994), pp. 175-186. 2005-04-25T21:38:25-00:00 1994 175 186 ACM Press collaborative filtering grouplens usenet http://www.citeulike.org/user/Jaykul/article/486168 <i>Communications of the ACM, Vol. 40, No. 3. (1997), pp. 77-87.</i><br /><br />This article discusses the challenges involved in creating a collaborative filtering system for Usenet news. The public trial of GroupLens invited users from over a dozen newsgroups selected to represent a cross-section of Usenet (listed in Table 1) to apply our news reader software to enter ratings and receive predictions (we provided GroupLens-adapted versions of Gnus, xrn, and tin). Over a seven-week trial starting February 8, 1996, we registered 250 users who submitted a total of 47,569... GroupLens: Applying Collaborative Filtering to Usenet News Joseph Konstan Bradley Miller David Maltz Jonathan Herlocker Lee Gordon John Riedl Communications of the ACM, Vol. 40, No. 3. (1997), pp. 77-87. 2006-01-30T21:25:30-00:00 1997 Communications of the ACM 40 3 77 87 collaborative filtering grouplens usenet http://www.citeulike.org/user/Jaykul/article/674977 <i>(1998), pp. 206-215.</i> Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents Joaquin Delgado Naohiro Ishii Tomoki Ura (1998), pp. 206-215. 2006-05-30T14:14:01-00:00 1998 206 215 Springer-Verlag classifier clustering collaborative filtering recommender http://www.citeulike.org/user/Jaykul/article/133456 <i>Commun. ACM, Vol. 30, No. 5. (May 1987), pp. 390-402.</i> Intelligent information-sharing systems Thomas Malone Kenneth Grant Franklyn Turbak Stephen Brobst Michael Cohen doi:10.1145/22899.22903 Commun. ACM, Vol. 30, No. 5. (May 1987), pp. 390-402. 2005-03-19T02:51:10-00:00 1987 Commun. ACM 0001-0782 30 5 390 402 ACM Press content-based filtering http://www.citeulike.org/user/Jaykul/article/486166 <i>Commun. ACM, Vol. 35, No. 12. (December 1992), pp. 61-70.</i> Using collaborative filtering to weave an information tapestry David Goldberg David Nichols Brian Oki Douglas Terry doi:10.1145/138859.138867 Commun. ACM, Vol. 35, No. 12. (December 1992), pp. 61-70. 2006-01-30T21:21:52-00:00 1992 Commun. ACM 0001-0782 35 12 61 70 ACM Press collaborative filtering http://www.citeulike.org/user/Jaykul/article/163527 <i>(01 September 1983)</i> Introduction to Modern Information Retrieval (McGraw-Hill Computer Science Series) Gerard Salton (01 September 1983) 2005-04-18T14:31:09-00:00 1983 McGraw-Hill Companies data-mining recommender search http://www.citeulike.org/user/Jaykul/article/798475 <i>(2005)</i> The Structure of Collaborative Tagging Systems Scott Golder Bernardo Huberman (2005) 2006-08-12T16:51:57-00:00 2005 bibtex-import bookmarking social tagging http://www.citeulike.org/user/Jaykul/article/797413 <i>(1999), pp. 228-237.</i><br /><br />. Self-organizing maps are an unsupervised neural network model which lends itself to the cluster analysis of high-dimensional input data. However, interpreting a trained map proves to be difficult because the features responsible for a specific cluster assignment are not evident from the resulting map representation. In this paper we present our LabelSOM approach for automatically labeling a trained self-organizing map with the features of the input data that are the most relevant ones for... Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets Andreas Rauber Dieter Merkl (1999), pp. 228-237. 2006-08-11T19:48:02-00:00 1999 228 237 clustering labelsom neuralnetworks som http://www.citeulike.org/user/Jaykul/article/679323 <i>(2002)</i><br /><br />The Self-Organizing Map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data. With our novel Growing Hierarchical SelfOrganizing Map presented in this paper we address... The growing hierarchical self-organizing maps: exploratory analysis of high-dimensional data A Rauber D Merkl M Dittenbach (2002) 2006-06-01T10:07:37-00:00 2002 ghsom machine-learning neuralnetworks som unsupervised http://www.citeulike.org/user/Jaykul/article/782315 <i>Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on (2006), pp. 388-393.</i><br /><br />There have been many attempts to construct web page recommender systems using collaborative filtering. But the domains these systems can cover are very restricted because it is very difficult to assemble user preference data to web pages, and the number of web pages on the Internet is too large. In this paper, we propose the way to construct a new type of web page recommender system covering all over the Internet, by using Folksonomy and Social Bookmark which are getting very popular in these days. Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions S Niwa Takuo Doi S Honiden Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on (2006), pp. 388-393. 2006-08-02T01:26:54-00:00 2006 Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on 388 393 collaborative data-mining filtering folksonomies recommender http://www.citeulike.org/user/Jaykul/article/452911 <i>Queue, Vol. 3, No. 9. (November 2005), pp. 28-35.</i> Social bookmarking in the enterprise David Millen Jonathan Feinberg Bernard Kerr doi:10.1145/1105664.1105676 Queue, Vol. 3, No. 9. (November 2005), pp. 28-35. 2005-12-29T17:03:58-00:00 2005 Queue 1542-7730 3 9 28 35 ACM Press bookmarking enterprise folksonomies recommendation social tagging http://www.citeulike.org/user/Jaykul/article/771854 <i>(1996)</i><br /><br />The Self-Organizing Map (SOM) represents the result of a vector quantization algorithm that places a number of reference or codebook vectors into a high-dimensional input data space to approximate to its data sets in an ordered fashion. The SOM PAK program package contains all programs necessary for the correct application of the SelfOrganizing Map algorithm in the visualization of complex experimental data. The first version 1.0 of this program package was published in 1992 and since then the package has been updated regularly to include latest improvements in the SOM implementations. SOM PAK: The Self-Organizing Map program package T Kohonen J Hynninen J Kangas J Laaksonen (1996) 2006-07-25T03:48:36-00:00 1996 bibtex-import kohonen programpackage som http://www.citeulike.org/user/Jaykul/article/771847 <i>(2005)</i><br /><br />Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f(x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates for real-world systems. The basic slope one scheme is suggested as a new reference scheme for collaborative filtering. By factoring in items that a user liked separately from items that a user disliked, we achieve results competitive with slower memory-based schemes over the standard benchmark EachMovie and Movielens data sets while better fulfilling the desiderata of CF applications. Slope One Predictors for Online Rating-Based Collaborative Filtering Daniel Lemire Anna Maclachlan (2005) 2006-07-25T02:43:35-00:00 2005 bibtex-import collaborative filtering independentstudy slopeone http://www.citeulike.org/user/Jaykul/article/762642 <i>Expert Systems with Applications, Vol. 25, No. 3. (October 2003), pp. 413-423.</i><br /><br />Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model, which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, Self-Organizing Map (SOM) and Case Based Reasoning (CBR) by changing an unsupervized clustering problem into a supervized user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference. The collaborative filtering recommendation based on SOM cluster-indexing CBR Tae Roh Kyong Oh Ingoo Han doi:10.1016/S0957-4174(03)00067-8 Expert Systems with Applications, Vol. 25, No. 3. (October 2003), pp. 413-423. 2006-07-18T00:19:27-00:00 2003 Expert Systems with Applications 25 3 413 423 clustering collaborative filtering independentstudy recommender som http://www.citeulike.org/user/Jaykul/article/748692 <i>Knowl. Inf. Syst., Vol. 7, No. 1. (2005), pp. 1-22.</i> Collaborative Filtering Using a Regression-Based Approach Slobodan Vucetic Zoran Obradovic doi:10.1007/s10115-003-0123-8 Knowl. Inf. Syst., Vol. 7, No. 1. (2005), pp. 1-22. 2006-07-09T20:13:04-00:00 2005 Knowl. Inf. Syst. 7 1 1 22 Springer-Verlag New York, Inc. filtering independentproject recommender social http://www.citeulike.org/user/Jaykul/article/712148 <i>(1999), pp. 439-446.</i> Combining collaborative filtering with personal agents for better recommendations Nathaniel Good Ben Schafer Joseph Konstan Al Borchers Badrul Sarwar Jon Herlocker John Riedl (1999), pp. 439-446. 2006-06-27T12:57:43-00:00 1999 439 446 American Association for Artificial Intelligence collaborative filtering recommendation http://www.citeulike.org/user/Jaykul/article/712144 <i>(1998), pp. 206-215.</i><br /><br />Next generation of intelligent information systems will rely on cooperative agents for playing a fundamental role in actively searching and finding relevant information on behalf of their users in complex and open environments, such as the Internet. Whereas relevant can be defined solely for a specific user, and under the context of a particular domain or topic. On the other hand shared “social ” information can be used to improve the task of retrieving relevant information, and for refining each agent’s particular knowledge. In this paper, we combine both approaches developing a new content-based filtering technique for learning up-to-date users ’ profile that serves as basis for a novel collaborative information-filtering algorithm. We demonstrate our approach through a system called RAAP (Research Assistant Agent Project) devoted to support collaborative research by classifying domain specific information, retrieved from the Web, and recommending these “bookmarks ” to other researcher with similar research interests. Intelligent Collaborative Information Retrieval: Actively Learning to Classify and Recommend Documents Joaquin Delgado Naohiro Ishii Tomoki Ura (1998), pp. 206-215. 2006-06-27T12:37:54-00:00 1998 206 215 Springer-Verlag classifier collaborative filtering recommendation http://www.citeulike.org/user/Jaykul/article/181742 <i>(2000), pp. 165-176.</i><br /><br />Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problems associated with more traditional Web personalization techniques such as collaborative or contentbased filtering. These problems include lack of scalability, reliance on subjective user ratings or static profiles, and the inability to capture a richer set of semantic relationships among objects (in content-based systems). Yet, usage-based personalization can be problematic when little usage data is ... Integrating Web Usage and Content Mining for More Effective Personalization Bamshad Mobasher Honghua Dai Tao Luo Yuqing Sun Jiang Zhu (2000), pp. 165-176. 2005-05-06T15:29:46-00:00 2000 165 176 data-mining personlization web http://www.citeulike.org/user/Jaykul/article/693352 <i>(2004), pp. 95-106.</i> An interactive clustering-based approach to integrating source query interfaces on the deep Web Wensheng Wu Clement Yu Anhai Doan Weiyi Meng doi:10.1145/1007568.1007582 (2004), pp. 95-106. 2006-06-12T04:20:18-00:00 2004 95 106 ACM Press clustering search ui web http://www.citeulike.org/user/Jaykul/article/695242 <i></i><br /><br />We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which can be used to compute the predictive distribution of a test point and the probability of it belonging to any of the existing clusters in the tree. (2) It uses a model-based criterion to... Bayesian Hierarchical Clustering Katherine Heller 2006-06-13T20:57:23-00:00 bayesian clustering hierarchical http://www.citeulike.org/user/Jaykul/article/712140 <i>Communications of the ACM, Vol. 40, No. 3. (1997), pp. 66-72.</i> Fab: content-based, collaborative recommendation Marko Balabanovic Yoav Shoham doi:10.1145/245108.245124 Communications of the ACM, Vol. 40, No. 3. (1997), pp. 66-72. 2006-06-27T12:09:31-00:00 1997 Communications of the ACM 40 3 66 72 ACM Press recommender social http://www.citeulike.org/user/Jaykul/article/1597 <i>(2004), pp. 210-217.</i> Learning to cluster web search results Hua-Jun Zeng Qi-Cai He Zheng Chen Wei-Ying Ma Jinwen Ma doi:10.1145/1008992.1009030 (2004), pp. 210-217. 2004-12-05T04:09:28-00:00 2004 210 217 ACM Press clustering search web http://www.citeulike.org/user/Jaykul/article/221107 <i>Information Processing Letters, Vol. 76, No. 4--6. (2000), pp. 175-181.</i><br /><br />We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A clustering algorithm based on graph connectivity Erez Hartuv Ron Shamir Information Processing Letters, Vol. 76, No. 4--6. (2000), pp. 175-181. 2005-06-06T20:55:33-00:00 2000 Information Processing Letters 76 4--6 175 181 algorithm clustering graph http://www.citeulike.org/user/Jaykul/article/709300 <i>Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on (2003), pp. 33-38.</i><br /><br />Recommender system is a kind of Web intelligence techniques to make a daily information filtering for people. Clustering techniques have been applied to the item-based collaborative filtering framework to solve the cold start problem. It also suggests a way to integrate the content information into the collaborative filtering. Extensive experiments have been conducted on MovieLens data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem. Clustering approach for hybrid recommender system Qing Li Byeong Kim Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on (2003), pp. 33-38. 2006-06-23T20:50:35-00:00 2003 Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on 33 38 clustering recommender http://www.citeulike.org/user/Jaykul/article/664041 <i>(2006), pp. 36-39.</i><br /><br />The panel will explore the relevance of the emerging tagging systems (Flickr, Del.icio.us, RawSugar and more). Why do they seem to work? What kinds of incentives are required for users to participate? Will tagging survive and scale to mass adoption? What are the behavioral, economic, and social models that underlie each tagging system? What are the dynamics of those systems, and how are they derived from the specific application's design and affordances?.We will demand answers to these questions and others from some of the pioneering practitioners and academics in the field. Bring your wireless laptop to participate in a live tagging experiment! The experiment results will be shown and discussed at the end of the panel. To add to the fun, parts of the discussion will be motivated by short video segments. Why do tagging systems work? George Furnas Caterina Fake Luis von Ahn Joshua Schachter Scott Golder Kevin Fox Marc Davis Cameron Marlow Mor Naaman doi:10.1145/1125451.1125462 (2006), pp. 36-39. 2006-05-22T07:30:23-00:00 2006 36 39 ACM Press folksonomies independentstudy msproject social tagging http://www.citeulike.org/user/Jaykul/article/111664 <i>(15 August 2002)</i><br /><br />Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort.<br><br>* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.<br>* Details the special challenges associated with analyzing unstructured and semi-structured data.<br>* Looks at how classical Information Retrieval techniques have been modified for use with Web data.<br>* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.<br>* Analyzes current applications for resource discovery and social network analysis.<br>* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul> Mining the Web: Analysis of Hypertext and Semi Structured Data Soumen Chakrabarti (15 August 2002) 2005-03-02T15:59:19-00:00 2002 Morgan Kaufmann classifier data-mining machine-learning web http://www.citeulike.org/user/Jaykul/article/378168 <i>Neural Process. Lett., Vol. 16, No. 2. (October 2002), pp. 93-109.</i> Self-Organizing Dynamic Graphs Ezequiel López-Rubio José Muñoz-Pérez José Gómez-Ruiz doi:10.1023/A:1019999727252 Neural Process. Lett., Vol. 16, No. 2. (October 2002), pp. 93-109. 2005-11-02T15:36:37-00:00 2002 Neural Process. Lett. 1370-4621 16 2 93 109 Kluwer Academic Publishers graphing som http://www.citeulike.org/user/Jaykul/article/703555 <i>Vol. 3 (July 2003)</i><br /><br />This paper describes the development and evaluation of two unsupervised learning mechanisms for solving the automatic document categorization problem. Both mechanisms are based on a hierarchical structure of Self-Organizing Feature Maps. Specifically, one architecture is based on the vector space model whereas the other one is based on a code-books model. Results show that the latter architecture performs better than the first as based on the quality of the returned clusters. A Comparison of SOM Based Document Categorization Systems Nur Vol. 3 (July 2003) 2006-06-20T17:49:09-00:00 2003 3 independentstudy msproject som tagsom http://www.citeulike.org/user/Jaykul/article/703554 <i>Queue, Vol. 3, No. 9. (2005), pp. 8-8.</i> Socially acceptable behavior Charlene O'Hanlon Queue, Vol. 3, No. 9. (2005), pp. 8-8. 2006-06-20T17:49:09-00:00 2005 Queue 3 9 8 8 ACM Press behavior independentstudy msproject social tagging http://www.citeulike.org/user/Jaykul/article/703553 <i>Queue, Vol. 3, No. 9. (November 2005), pp. 28-35.</i> Social bookmarking in the enterprise David Millen Jonathan Feinberg Bernard Kerr Queue, Vol. 3, No. 9. (November 2005), pp. 28-35. 2006-06-20T17:49:09-00:00 2005 Queue 3 9 28 35 ACM Press delicious enterprise independentstudy msproject tagging http://www.citeulike.org/user/Jaykul/article/703552 <i>Spectrum, IEEE, Vol. 43, No. 2. (2006), pp. 80-80.</i><br /><br />Web geeks have long fantasized about a Web taxonomy, a classification scheme that would encompass the entire Web — not just sites, but also content such as images and blog posts... At sites such as Flickr, del.icio.us, and Furl, ordinary users are creating their own taxonomic schemes... Technically Speaking: Folk Wisdom P Mcfedries Spectrum, IEEE, Vol. 43, No. 2. (2006), pp. 80-80. 2006-06-20T17:49:09-00:00 2006 Spectrum, IEEE 43 2 80 80 folksonomy independentstudy msproject tagging http://www.citeulike.org/user/Jaykul/article/703551 <i>(December 2004)</i><br /><br />This paper examines user-generated metadata as implemented and applied in two web services designed to share and organize digital media to better understand grassroots classification. Metadata - data about data - allows systems to collocate related information, and helps users find relevant information. The creation of metadata has generally been approached in two ways: professional creation and author creation. In libraries and other organizations, creating metadata, primarily in the form of catalog records, has traditionally been the domain of dedicated professionals working with complex, detailed rule sets and vocabularies. The primary problem with this approach is scalability and its impracticality for the vast amounts of content being produced and used, especially on the World Wide Web. The apparatus and tools built around professional cataloging systems are generally too complicated for anyone without specialized training and knowledge. A second approach is for metadata to be created by authors. The movement towards creator described documents was heralded by SGML, theWWW, and the Dublin Core Metadata Initiative. There are problems with this approach as well - often due to inadequate or inaccurate description, or outright deception. This paper examines a third approach: user-created metadata, where users of the documents and media create metadata for their own individual use that is also shared throughout a community. Folksonomies - Cooperative Classification and Communication Through Shared Metadata Adam Mathes (December 2004) 2006-06-20T17:49:09-00:00 2004 critique delicious folksonomies independentstudy msproject review tagsom http://www.citeulike.org/user/Jaykul/article/703550 <i>(1998), pp. 310-315.</i> WEBSOM - Self-Organizing Maps of Document Collections Timo Honkela Samuel Kaski Krista Lagus Teuvo Kohonen (1998), pp. 310-315. 2006-06-20T17:49:09-00:00 1998 310 315 Helsinki University of Technology, Neural Networks Research Centre browsing independentstudy interface msproject search som ui web websom http://www.citeulike.org/user/Jaykul/article/703548 <i>(2005)</i> MailSOM - Visual Exploration of Electronic Mail Archives Using Self-Organizing Maps Schreck (2005) 2006-06-20T17:49:08-00:00 2005 classifier independentstudy mailsom msproject som spam http://www.citeulike.org/user/Jaykul/article/703547 <i>(March 2005)</i><br /><br />Retrieving relevant documents over the Web is a difficult task. Currently, search engines rely on keywords for matching documents to user queries. This paper explores the potential for discriminating documents based on the genre of the document. I define genre as a taxonomy that incorporates the style, form and content of a document which is orthogonal to topic, with fuzzy classification to multiple genres. I explore how to automate the classification of Web documents according to their genres. Over 1,600 features of genres are identified and selection methods examined for distinguishing documents between ten genre types. Classification of documents using Bayes Net on a subset of 75 features achieved 90% accuracy. Stereotyping the Web: Genre Classification of Web Documents Elizabeth Boese (March 2005) 2006-06-20T17:49:08-00:00 2005 Computer Science Department, Colorado State University classifier independentstudy msproject taxonomy thesis web http://www.citeulike.org/user/Jaykul/article/703546 <i>(2003), pp. 295-302.</i> Bootstrapping for hierarchical document classification Giordano Adami Paolo Avesani Diego Sona doi:10.1145/956863.956920 (2003), pp. 295-302. 2006-06-20T17:49:08-00:00 2003 295 302 ACM Press classifier independentstudy msproject http://www.citeulike.org/user/Jaykul/article/352480 <i>(1 June 2005)</i><br /><br />1. Introducing you to foundational information theory concepts. 2. Implementing these foundational concepts as classes using PHP and SQL. 3. Using these classes to mine web data. This introduction will focus on forging theoretical and practical connections between information theory and database theory. An appreciation of these linkages opens up the possibility of using information theory concepts as a foundation for the design of data mining tools. We will take the first steps down that path. Calculating Entropy for Data Mining Paul Meagher (1 June 2005) 2005-10-17T00:27:44-00:00 2005 O'Reilly data-mining entropy onlamp php http://www.citeulike.org/user/Jaykul/article/351790 <i>(28 May 2000)</i><br /><br />Knowledge Discovery and Data mining (KDD) emerged as a rapidly growing interdisciplinary field that merges together databases, statistics, machine learning and related areas in order to extract valuable information and knowledge in large volumes of data. With the rapid computerization in the past two decades, almost all organizations have collected huge amounts of data in their databases. These organizations need to understand their data and/or to discover useful knowledge as patterns and/or models from their data. This course aims at providing fundamental techniques of KDD as well as issues in practical use of KDD tools. It will show how to achieve success in understanding and exploiting large databases by: uncovering valuable information hidden in data; learn what data has real meaning and what data simply takes up space; examining which data methods and tools are most effective for the practical needs; and how to analyze and evaluate obtained results. The course is designed for the target audience such as specialists, trainers and IT users. It does not assume any special knowledge as background. Understanding of computer use, databases and statistics will be helpful. Knowledge Discovery and Data Mining Techniques and Practice Tu Bao (28 May 2000) 2005-10-16T00:18:16-00:00 2000 data-mining kdd knowledge-discovery http://www.citeulike.org/user/Jaykul/article/161814 <i>(09 August 2001)</i><br /><br />During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes theimprtant ideas in these areas ina common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a vluable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie Robert Tibshirani Jerome Friedman (09 August 2001) 2005-04-15T14:57:05-00:00 2001 Springer book compsci data-mining machine-learning statistics