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Topic: Relational data mining

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	Saso (Site not responding. Last check: 2007-11-03)
		Relational Data Mining (RDM) is the multi-disciplinary field dealing with knowledge discovery from relational databases consisting of multiple tables (relations).
		Mining data which consists of complex/structured objects also falls within the scope of this field: the normalized representation of such objects in a relational database requires multiple tables.
		Typically, data mining algorithms have been upgraded from the single-table case: for example, distance-based algorithms for prediction and clustering have been upgraded by defining distance measures between examples/instances represented in relational logic.
	www.siam.org /meetings/sdm03/tutorials/saso.htm (555 words)

	Qwika - similar:Data-Mining
		A data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision-making tasks, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favour efficient data analysis and reporting (especially OLAP)...
		Data stream mining is the process of extracting knowledge structures from continuous, rapid data records.
		Wyoming coal mine Coal mining is the extraction of coal from the Earth for use as fuel.
	www.qwika.com /rels/Data-Mining (1594 words)

	PharmaDM - Relational data mining
		Relational data mining has an unrivalled expressivity so that patterns that it finds can emerge as very complex English logical rules, e.g.
		Relational data mining no longer requires biochemists/biologists to master 'computer languages'.
		Because of the ability to mine complex relational databases directly, background knowledge can easily be incorporated in the simple form of extra tables in the database.
	www.pharmadm.com /tech_datamintech.asp (326 words)

	Relational data mining - Wikipedia, the free encyclopedia
		Relational data mining is a data mining technique for relational databases.
		Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns).
		The most important theoretical foundation of relational data mining is inductive logic programming.
	en.wikipedia.org /wiki/Relational_data_mining (130 words)

	Introduction to Data Mining with SQL Server (Part 2) (Site not responding. Last check: 2007-11-03)
		This type of data mining algorithm is best used in situations where business users or analysts require the mining of a large series of transactional or organizational data to observe patterns and groupings to gain insight into tailoring the business focus to new opportunities.
		Once a selection of organizational data is prepared for data mining, a mining algorithm is chosen based on the type of data mining desired, and a training case set is identified, architects are ready to create the data mining model and subsequently train it using a series of training cases.
		Data mining allows the business to free the this information that is inherent in their operational data and present analysis, users, decision makers and other business processes through applications, with true decision support.
	www.sql-server-performance.com /ec_data_mining2.asp (3032 words)

	Training Data Mining Models (Site not responding. Last check: 2007-11-03)
		In the Mining Model Wizard, the source tables or cube used to construct the model are assumed to contain training data and are used to supply the data mining model.
		In the Mining Model Editor, the case and association tables for a relational data mining model expected to supply the training data are displayed as part of the model.
		After the mining model is processed, the information about the patterns and rules discovered in the training data are stored as data mining model content, along with the distribution information of the case data as well.
	doc.ddart.net /mssql/sql2000/html/olapdmad/agdatamining_3e2b.htm (383 words)

	Editing Relational Data Mining Models (Analysis Services (SQL Server))
		Relational Mining Model Editor can also be used to process a data mining model and view the resulting content.
		Relational Mining Model Editor enables you to change basic properties, such as the data mining algorithm, of the data mining model.
		Relational Mining Model Editor also displays the table schema used to construct the case set in the Mining Model Wizard, showing both case and supporting tables.
	msdn.microsoft.com /library/en-us/olapdmad/agdatamining_1wmr.asp?frame=true (172 words)

	Qudata.com - Abstracts for Data mining resources
		The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of rel-relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known in-intrusions.
		In general, data mining methods such as neural networks and decision trees can be a useful addition to the techniques available to the financial analyst.
		However, the data mining techniques tend to require more historical data than the standard models and, in the case of neural networks, can be difficult to interpret.
	qudata.com /lib/data_mining (3455 words)

	Data Mining - Lecture 15: (Site not responding. Last check: 2007-11-03)
		The interpretive nature of relationship-identification means that any application of relational data mining always offers us an implicit recoding of the data, i.e., an encoding in which combinations of values in a particular relationship are replaced by the relevant value of the relationship.
		Relational regularities appearing at some derived level may be described as `compositional' on the grounds that their identification is based on a prior identification of multi-part entities.
		The relational end of data mining is important, then, if only because it allows a connection to be made between the engineering-oriented processes of function acquisition and the more obviously cognitive issues relating to perception, learning and knowledge acquisition.
	www.cogs.susx.ac.uk /users/christ/crs/dm/lec15.html (1408 words)

	Building Data Mining Models
		This property is used to optimize the mining model by giving the mining model algorithm some indication of the statistical nature of the data in the column.
		Locking the data mining model during processing prevents access by other users until the mining model is unlocked, improving performance during the training of the mining model and ensuring that repository integrity is maintained.
		Because the structure of the OLAP data mining model is drawn from the structure of the source cube, all source OLAP objects used by the mining model must be visible to the mining model.
	msdn2.microsoft.com /en-us/library/ms133839.aspx (2858 words)

	CS 590D: Data Mining
		Data mining is a hot area, now is your chance to get involved.
		Data Mining has emerged at the confluence of machine learning, statistics, and databases as a technique for discovering summary knowledge in large datasets.
		The emphasis will be on algorithmic issues and data mining from a data management and machine learning viewpoint, it is anticipated that students interested in additional study of data mining will benefit from taking offerings in statistics such as Stat 598M or Stat 695A.
	www.cs.purdue.edu /homes/clifton/cs590d (2646 words)

	Scalability and Efficiency in Multi-Relational Data Mining - Blockeel, Sebag (ResearchIndex) (Site not responding. Last check: 2007-11-03)
		Abstract: Efficiency and scalability have always been important concerns in the field of data mining, and are even more so in the multi-relational context, which is inherently more complex.
		34 Propositionalization approaches to relational data mining (context) - Kramer, Lavra et al.
		8 Relational Data Mining (context) - zeroski, Lavra et al.
	citeseer.ist.psu.edu /blockeel03scalability.html (1236 words)

	Data Mining: Text Mining, Visualization and Social Media
		With the increasing availability of building models and the greater detail in evaluation data, the ability to accurately demonstrate to the user the environmental differences that lighting can bring to a scene will the users ability to understand and explore new environments.
		Adding a user controlled temporal element would also broaden the types of data and their exploration (imagine browsing historical weather data on the globe).
		My hope is that eventually the web, data mining and online applications will make arranging vacations (especially to the beach) trivial.
	datamining.typepad.com (1464 words)

	Xiaoxin Yin's Home Page
		I am a member of Data Mining Research Group, and my advisor is Dr.
		My research is focused on Multi-relational Data Mining, which includes a variety of techniques for discovering knowledge directly from relational databases, including classification, clustering, association analysis, object search, record linkage etc.
		Relational Dataset Generator: a relational dataset generator for relational classification, which has been used by CrossMine.
	www.ews.uiuc.edu /~xyin1 (867 words)

	Data Mining
		Data mining is the extraction of useful knowledge from large bodies of data.
		Relational Markov Models and their Application to Adaptive Web Navigation, with Corin Anderson and Dan Weld.
		Mining Time-Changing Data Streams, with Geoff Hulten and Laurie Spencer.
	data.cs.washington.edu /mining.html (1477 words)

	Safarii Multi-Relational Data Mining Environment
		Safarii is a state-of-the-art Data Mining environment for analysing large relational databases.
		As Safarii works directly on the relational database, without requiring extensive pre-processing, it can be easily used by non-expert users such as managers and domain experts.
		As most Data Mining tools, Safarii mines a given database for subgroups with interesting or surprising characteristics (customers with a high response rate to a mailing, website visitors with above-average spending, a class of chemical compounds with increased carcinogenic potential).
	www.kiminkii.com /safarii.html (577 words)

	Relational Data Mining with Inductive Logic Programming for Link Discovery - Mooney, Melville, Tang, Shavlik, Dutra, ...
		Link discovery concerns the identification of complex relational patterns that indicate potentially threatening activities in large amounts of relational data.
		Most data-mining methods assume data is in the form of a feature-vector (a single relational table) and cannot handle multi-relational data.
		Relational data mining with inductive logic programming for link discovery.
	citeseer.ist.psu.edu /659212.html (853 words)

	Data mining - Wikipedia, the free encyclopedia
		Data mining (DM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns using association rules.
		Metadata, or data about a given set of data, are often expressed in a condensed data mine-able format, or one that facilitates the practice of data mining.
		Point Horizon is an integrated data exploration, analysis, visualization and forcasting application with emphasis in dynamical methods.
	en.wikipedia.org /wiki/Data_mining (1978 words)

	Data Mining Project
		The Knowledge Discovery and Data Mining (KDD) process consists of data selection, data cleaning, data transformation and reduction, mining, interpretation and evaluation, and finally incorporation of the mined "knowledge" with the larger decision making process.
		Scalable Data Mining Algorithms: We are exploring scalable algorithms for modeling large databases.
		Read more about how data mining is integrated into SQL server.
	research.microsoft.com /dmx/datamining (516 words)

	Relational Data Mining School (Site not responding. Last check: 2007-11-03)
		-------------------------------------------------------------------------------- Relational Data Mining Summer School 17 and 18 August 2002, Helsinki, Finland (Just before ECML/PKDD-2002) http://www-ai.ijs.si/SasoDzeroski/RDMSchool/ [Apologies if you receive multiple copies of this message.] -------------------------------------------------------------------------------- Relational Data Mining (RDM) is the multi-disciplinary field dealing with knowledge discovery from relational databases consisting of multiple tables.
		The field aims at integrating results from existing fields such as inductive logic programming (ILP), KDD, data mining, machine learning and relational databases; producing new techniques for mining multi-relational data; and practical applications of such tecniques.
		The Summer School on Relational Data Mining will provide a comprehensive introduction to the techniques and applications of relational data mining by leading experts in the field.
	www.cleverset.com /advanced_r_and_d/archive/2002/msg00289.html (385 words)

	Data Warehouse, Data Mining, Data Mart from Cincinnati's Swordfish Computer Solutions
		Data mart components that will include data mining, BI, and other tools to sit on top of the data warehouse/relational database allowing data mining and BI logic to generate data mining results.
		Dan is based out of Cincinnati and has focused on data warehouse, data mart, and data mining systems since 1979.
		The needs assessment is the most critical phase of the data management system because it lays the essential foundation for successful data warehousing, data mart, and data mining.
	www.swordfishcs.com /Pages/Cincinnati-Data-Warehouse-Mining.htm (419 words)

	Relational Data Mining
		Relational data mining studies methods for knowledge discovery in databases when the database has information about several types of objects.
		Hence there is little doubt as to the relevance of the area; indeed, one can wonder why most of data mining research has concentrated on the single table case.
		Relational data mining has its roots in inductive logic programming, an area in the intersection of machine learning and programming languages.
	www-ai.ijs.si /SasoDzeroski/RDMBook (155 words)

	Relational Databases - Homepage for Mining Structured Data
		Data mining the yeast genome in a lazy functional language.
		enumerate all hierarchical frequent queries given a user defined mode bias and a relational database with hierarchies on the objects in the database.
		Discovery of Spatial Association Rules in Georeferenced Census Data: A Relational Mining Approach.
	hms.liacs.nl /ilp.html (593 words)

	580Syllabus (Site not responding. Last check: 2007-11-03)
		Description: Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data.
		The knowledge discovery process includes data selection, cleaning, coding, using different statistical, pattern recognition and machine learning techniques, and reporting and visualization of the generated structures.
		Data Mining on Symbolic Knowledge Extracted from the Web - a paper describing an approach to Web Mining.
	www.cs.ccsu.edu /~markov/ccsu_courses/DataMining.html (785 words)

	››› buch.de - bücher - versandkostenfrei - From Inductive Logic Programming to Multi-Relational ...
		These subfields of data mining and machine learning are concerned with analyzing structured data that arise in numerous applications, such as bioinformatics, Web mining, natural language processing, etc.
		Related systems and techniques are covered in detailed bibliographies in each chapter.
		The book addresses graduate students in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning.
	www.buch.de /buch/06331/851_from_inductive_logic_programming_to_multi_relational_data_mining__cognitive_technologies.html (402 words)

	Penn Data Mining Group: Publications
		Statistical Relational Learning for Document Mining, Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock, In Proceedings of IEEE International Conference on Data Mining (ICDM 2003).
		Towards Structural Logistic Regression: Combining Relational and Statistical Learning, Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock, Workshop on Multi-Relational Data Mining at KDD 2002.
		mining the Bibliome (Information Extraction from the Biomedical Literature), we are annotating medline documents with entities and their relations, and using machine learning methods to do automatic tagging and information extraction.
	www.cis.upenn.edu /group/datamining/publications.html (1802 words)

	Data Mining and Knowledge Discovery Websites
		KDnuggets.com is the leading and most comporehensive website for Data Mining and Knowledge Discovery, but here are other websites related to Data Mining and Knowledge Discovery.
		AAAI topics on Data Mining and Discovery, a collections of links to publications.
		Data Mine, by Andy Pryke, a portal for data mining.
	www.kdnuggets.com /websites/data-mining.html (455 words)

	Welcome to the Home Page of Data Mining Group: CoMMA Project
		The goal of the project is to address this problem and employ the current application as a test bed.
		We generate association rules on image data (the RGBY values), and on text data separately.
		In Proceedings of 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000.
	www.cs.rit.edu /~dmrg/CoMMA (363 words)

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