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gSpan: Graph-Based Substructure Pattern Mining - Yan, Han (ResearchIndex) gSpan: Graph-Based Substructure Pattern Mining - Yan, Han (ResearchIndex) The semantics of frequent subgraphs: Mining and navigation.. 45 gspan: Graph-based substructure pattern mining - Yan, Han - 2002 DBLP citeseer.ist.psu.edu /yan02gspan.html   (394 words)

Large Scale Data Mining and Pattern Recognition: Overview By applying and extending ideas from data mining and pattern recognition, we are developing a new generation of computational tools and techniques that are being used to improve the way in which scientists extract useful information from data. Patterns in data are identified using measurable features or attributes that have been extracted from the data. Data mining is an interactive and iterative process involving data pre-processing, search for patterns, knowledge evaluation, and possible refinement of the process based on input from domain experts or feedback from one of the steps. www.llnl.gov /CASC/sapphire/overview   (1536 words)

Pattern Mining Strictly speaking mining is where a group of people look for patterns and the workshop is where individuals review pattern written by others. The reason I am using the term "Mining" instead of "Workshop" is because of the nature of CTG with deverse locations and sources of patterns. Patterns should not only provide facts (like a reference manual or user's guide), but should also tell a story which captures the experience they are trying to convey. home.earthlink.net /~ronhowe1/mining/mining.htm   (363 words)

Pattern crafting - focus group While studying pattern mining process and in particular its first stage – pattern crafting – it is worth noting that the first draft of a pattern usually reflects experience of a small group of people working in a team on the same project or in the same organisation. Later the pattern is improved by a shepherd and pattern workshop contributors. Note: in the follow-up interview the practitioner completely ignored pattern skeletons presented to her since she was not familiar with patterns – she preferred to share her experience on the outlined issues without referring to pattern sections, however she answered interviewer’s questions referring to pattern sections (as shown above). www.dis.unimelb.edu.au /staff/tanya/Publications/EuroPLOP2005_FocusGroup.htm   (817 words)

Pattern mining - Wikipedia, the free encyclopedia Pattern mining is the task of finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the need to analyze supermarket transaction data, that is, to examine customer behaviour in terms of the purchased products. en.wikipedia.org /wiki/Pattern_mining   (162 words)

Patterns: Patterns Mining   (Site not responding. Last check: ) After the patterns were documented, we tried, with varying degrees of success, to return to our sources to verify that what we had written really captured the speaker's intent. Writing patterns is difficult and those who have struggled to capture their experience in a pattern are in a good position to help others who have chosen the same path. One pattern I mined from the Tango class is "What have you done for me lately?" The essence of this pattern is that a company can't bank points with developers who have high expectations. members.cox.net /risingl1/articles/mining.htm   (5085 words)

Temporal-Spatial Pattern Mining We first develop the theory for automatic spatial pattern modeling and extraction to learn a probabilistic parametric model from the attributed relational graphs of multiple samples of a pattern. The pattern ARG models are assumed to be Contextual Gaussian Mixture models. The pattern ARG model is assumed to have two components. www.cs.brandeis.edu /~hong/Research/Pattern_Modeling_Detection/temporal_spatial_pattern.htm   (581 words)

Mining Sequential Patterns The algorithm we use to mine the patterns is similar to the Apriori Algorithm as described in Fast Algorithms for Mining Association Rules. The mining program has been made available to the research community under the terms of the GNU General Public License. When combining these shifted patterns, the new count is taken as the maximum of the counts of the combined patterns. www.cs.cornell.edu /database/himalaya/SequentialPatterns/seqPatterns_main.htm   (1622 words)

ApproxMAP : Analyzing Sequence of sets Sequential pattern mining is an important data mining task with broad applications. Thus, the dominant pattern is generated by selecting all items in the weighted sequence that have a weight more than 40% of the cluster. Consensus Patterns : similar to consensus strings in computational biology literature, it is the underlying sequential pattern in a group of similar sequences. www.unc.edu /~kum/approxMAP   (680 words)

Sequential Pattern Mining on ASL   (Site not responding. Last check: ) The aim of this research project is to develop and test methods for indexing, retrieval, and data mining of human motion trajectories in video databases. The problem of mining sequential patterns and the support-confidence framework were originally prosposed by Agrawal and Srikant. Some papers on Sequential Pattern Mining with brief summaries can be found here. cs-people.bu.edu /panagpap/Research/asl_mining.htm   (414 words)

System and method for constraint based sequential pattern mining (US6473757) A Regular Expression (RE) is used for identifying the family of interesting frequent patterns. A family of methods that enforce the RE constraint to different degrees within the generating and pruning of candidate patterns during the mining process is utilized. This is accomplished by employing different relaxations of the RE constraint in the mining loop. www.delphion.com /details?pn=US06473757__   (292 words)

Wiley::Mining Graph Data This text takes a focused and comprehensive look at an area of data mining that is quickly rising to the forefront of the field: mining data that is represented as a graph. Following the authors' step-by-step guidance, even readers with minimal background in analyzing graph data will be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets. This landmark work is intended for students and researchers in computer science, information systems, and data mining who want to learn how to analyze and extract useful patterns and concepts from graph data. www.wiley.com /WileyCDA/WileyTitle/productCd-0471731900.html   (412 words)

Mining Sequential Patterns with Regular Expression Constraints   (Site not responding. Last check: ) Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. www.cs.umd.edu /areas/db/seminar/fall99/01-mining-seq-patterns.html   (257 words)

Pattern Mining Thread Every pattern that could be observed by even the most intricate observation and "pattern mining" of every instance ever created by Mr. I think patterns are about as proprietary as Newton's laws of physics - if it were left to "modern" legal beagles, I think half of the extant corpus of math and science would remain shrouded in secrecy under the belief that there's something "proprietary" about it. The key thing here is "in the code to be mined." What I was suggesting is that the people who wrote the original code (the code that is to be mined) and have written similar things in the past, are the people to mine that piece of code. c2.com /cgi/wiki?PatternMiningThread   (2874 words)

PAKDD-2001 - Tutorials - Monday April 16, 2001 In contrast to conventional data mining systems that are concerned primarily with high predictive accuracy of discovered patterns, natural induction systems also stress the ease of the patterns' interpretation and understandability. Presented ideas will be illustrated by examples of their application to pattern discovery in a large medical database and in a temporal database characterizing behaviour of computer users. Sequential pattern mining, i.e., discovering frequent sub-sequences in sequence databases, is an important data mining task. www.csis.hku.hk /pakdd01/page-tutorial.htm   (3384 words)

WWW2006 - A Probabilistic Approach to Spatiotemporal Theme Pattern Mining on Weblogs Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we define the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2) generating theme life cycles for each given location; and (3) generating theme snapshots for each given time period. www.www2006.org /programme/item.php?id=544   (263 words)

Patricio Galeas - Web Mining Web content mining is the process of extracting knowledge from the content of documents or their descriptions. There are two groups of web content mining strategies: Those that directly mine the content of documents and those that improve on the content search of other tools like search engines. Applying data mining techniques on access logs unveils interesting access patterns that can be used to restructure sites in a more efficient grouping, pinpoint effective advertising locations, and target specific users for specific selling ads. www.galeas.de /webmining.html   (1940 words)

Hillside.net - Pattern Mining After the pattern has been modified, the pattern should be publicly posted for comments by all members of the development community. All patterns are living documents that grow and evolve to more useful forms. After a suitable exposure, the pattern could become part of a "Best Practices Handbook." Collections of patterns from this handbook could be part of the standard training program for new hires. hillside.net /patterns/patternsmining.htm   (243 words)

WWW2006 - A Probabilistic Approach to Spatiotemporal Theme Pattern Mining on Weblogs Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we define the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2) generating theme life cycles for each given location; and (3) generating theme snapshots for each given time period. www2006.org /programme/item.php?id=544   (263 words)

Sequential PAttern Mining using A Bitmap Representation - Ayres, Flannick, Gehrke, Yiu (ResearchIndex) Our algorithm is especially efficient when the sequential patterns in the database are very long. Using Convolution to Mine Obscure Periodic Patterns In One.. 0.6: Mining Algorithms for Sequential Patterns in Parallel.. citeseer.ist.psu.edu /ayres02sequential.html   (408 words)

O'Reilly - Safari Books Online - 0131008250 - Bioinformatics Computing Due to the vast amount of information available, various data mining techniques have been developed over the years to assist in finding the data that a researcher is looking for. A number of data mining techniques such as hidden Markov Models, Decision Trees, Neural Networks and Genetic Algorithms are talked about and the pro's and con's of each one is discusses in detail. A bioinformatician needs to be at least aware of the various data mining techniques and should have an overview how they work and why they work in general. safari.oreilly.com /0131008250   (1534 words)

Recognition of Visual Invariants   (Site not responding. Last check: ) This new approach has not been implemented yet; details on the (not too serious) obstacles in the way of practical implementation will be mentioned at the end. The basic idea underlying George and Hawkins’ work is that object recognition and other aspects of visual-invariant-recognition occur via the hierarchical recognition of patterns in temporal sequences. I believe that the result of this would be an object recognition (and general visual invariant recognition) system with a high level of accuracy and generality. www.goertzel.org /dynapsyc/2004/ProbabilisticVisionProcessing.htm   (1597 words)

Sequential Pattern Mining in Multiple Streams In this paper, we deal with mining sequential patterns in multiple data streams. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILE¹, an efficient algorithm to facilitate the mining process. MILE recursively utilizes the knowledge of existing patterns to avoid redundant data scanning, and can therefore effectively speed up the new patterns’ discovery process. csdl2.computer.org /persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/icdm/2005/2278/00/2278toc.xml&DOI=10.1109/ICDM.2005.122   (195 words)

IngentaConnect Sequential Pattern Mining in Multi-Databases via Multiple Alignme...   (Site not responding. Last check: ) To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. Then, consensus patterns are mined directly from each cluster through multiple alignment. www.ingentaconnect.com /content/klu/dami/2006/00000012/F0020002/00000017   (321 words)

KDD-2001 tutorial: Scalable Frequent-Pattern Mining Methods: An Overview It has been an active theme of research in data mining, with broad applications in industry and deep implications in other themes of data warehousing and data mining. Although many efficient frequent-pattern mining techniques have been developed in the last 7-8 years, such as those listed at the end of this proposal, most of them have been published in scattered conference proceedings in several fields. For the spectrum of frequent-pattern mining, it covers mining associations, correlations, sequential patterns, max- and closed-patterns, partial periodicity, etc., and their applications in classification, data warehousing, spatial databases, multimedia databases, time-series databases, text databases, and WWW. www.acm.org /sigkdd/kdd2001/Tutorials/kdd2001_T5.html   (530 words)

DiSC - SPIRIT: Sequential Pattern Mining with Regular Expression Constraints In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Rakesh Agrawal, Ramakrishnan Srikant : Mining Sequential Patterns. www.sigmod.org /sigmod/disc/p_spiritsequentiamirak.htm   (390 words)

Main Page - Ajax Patterns AjaxPatterns.org began as a collection of design patterns, which formed the basis of the book, Ajax Design Patterns, and grew into a publicly editable wiki on anything and everything Ajax. There are 70+ Ajax patterns here, most linking to the original draft version for the design patterns book. Foundational Technology Patterns are lower-level patterns and a suitable introduction for people learning Ajax for the first time. ajaxpatterns.org   (451 words)

Protein sequence pattern mining with constraints. — Centro de Ciências e Tecnologias da Computação gIL was developed for linear sequence pattern mining and results from the combination of some of the most efficient techniques used in sequence and itemset mining. The algorithm exhibits a high adaptability, yielding a smooth and direct introduction of various types of features into the mining process, namely the extraction of rigid and arbitrary gap patterns. The use of constraints has also proved to be a very useful tool to specify user interesting patterns. cctc.di.uminho.pt /publications/pub-2005-037   (182 words)

Elder Research: Data Mining Consulting ACM Conference on Knowledge Discovery and Data Mining (KDD-2007) From Trees to Forests and Rule Sets -- A Unified Overview of Ensemble Methods, Tutorial, San Jose, CA, August 12, 2007 The 5th IEEE International Conference on Data Mining, Keynote, Houston, TX, November 27-30, 2005 The principals are active researchers in Data Mining, contributing to the literature of this emerging field in books, conferences, and through highly-regarded short courses and training seminars. www.datamininglab.com   (384 words)

Patterns Mining The topic of the chapter was patterns mining. Patterns are living things that change as we learn more about the problem, the context, the solution. These patterns should be gracefully retired as new ones spring up to replace them. users.rcn.com /jcoplien/oopsla/linda.html   (1637 words)

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