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Data Mining with Graphical Models [chapter]

Rudolf Kruse, Christian Borgelt
2002 Lecture Notes in Computer Science  
In this paper we study a popular technique from its arsenal of methods to do dependency analysis, namely learning inference networks (also called "graphical models") from data.  ...  Therefore a new line of research has recently been established, which became known under the names "Data Mining" and "Knowledge Discovery in Databases".  ...  Learning graphical models from data.  ... 
doi:10.1007/3-540-36169-3_3 fatcat:xuvjjmaddrbzzhbl37a3h3rc7a

Data mining with graphical models [chapter]

Rudolf Kruse, Christian Borgelt
1998 Lecture Notes in Computer Science  
In this paper we study a popular technique from its arsenal of methods to do dependency analysis, namely learning inference networks (also called "graphical models") from data.  ...  Therefore a new line of research has recently been established, which became known under the names "Data Mining" and "Knowledge Discovery in Databases".  ...  Learning graphical models from data.  ... 
doi:10.1007/bfb0095424 fatcat:umpdpvtkkncljfr3e3fewzqs3m

Data Mining with Graphical Models [chapter]

Rudolf Kruse, Christian Borgelt
2002 Lecture Notes in Computer Science  
In this paper we study a popular technique from its arsenal of methods to do dependency analysis, namely learning inference networks (also called "graphical models") from data.  ...  Therefore a new line of research has recently been established, which became known under the names "Data Mining" and "Knowledge Discovery in Databases".  ...  Learning graphical models from data.  ... 
doi:10.1007/3-540-36182-0_2 fatcat:6eaidf2ok5a4hi5nj4ldne4gby

Temporal causal modeling with graphical granger methods

Andrew Arnold, Yan Liu, Naoki Abe
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
With the surge of interest in model selection methodologies for regression, such as the Lasso, as practical alternatives to solving structural learning of graphical models, the question arises whether  ...  Recently graphical modeling with the concept of "Granger causality", based on the intuition that a cause helps predict its effects in the future, has gained attention in many domains involving time series  ...  INTRODUCTION Statistical modeling and data mining methods are playing an increasingly critical role in real world applications that involve forecasting and prediction.  ... 
doi:10.1145/1281192.1281203 dblp:conf/kdd/ArnoldLA07 fatcat:5uka4e6ffvbu3ffhfnyqwplxh4

Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control [chapter]

Jose M. Peña
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics  
The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data.  ...  We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs).  ...  Introduction Some models that have received increasing attention from the bioinformatics community as a means to gain insight into gene networks are Gaussian graphical models (GGMs) and variations thereof  ... 
doi:10.1007/978-3-540-78757-0_15 dblp:conf/evoW/Pena08 fatcat:sfnyg5mc7va3bestbc6jgcefuu

Graph-based Educational Data Mining

Collin F. Lynch, Tiffany Barnes, Linting Xue, Niki Gitinabard
2017 Educational Data Mining  
Also, graphical model techniques (e.g. Hidden Markov Models or probabilistic graphical models) has become more and more important to analyze educational data.  ...  Researchers are encouraged to discuss prior analyses of graph data and educational data analyses based on graphical models.  ...  We will open the workshop with a summary of prior meetings. We will spend the morning on presentations with a short discussion session before lunch.  ... 
dblp:conf/edm/LynchBXG17 fatcat:rdgcxjcklbcrpnlrce42xyemmy

Researches on the Prototype Implementation of Visual Data Mining Techniques

Guiliang Feng, Zhonghua Li, LianChun Zhang
2014 International Journal of Database Theory and Application  
The first part introduces the implementation technology of visual data mining technology prototype system and then describes the overall design of the architecture and features of visual data mining technology  ...  Research achievements of this article provides a useful reference to optimize visual data mining techniques.  ...  The Graphical Visualization of Data Mining Model Results The graphical visualization of data mining results model uses a combination of the chart to show, the results of different data mining models correspond  ... 
doi:10.14257/ijdta.2014.7.6.12 fatcat:jmo6rrhmnbgkfhtuytd426bd6u

Development of coal geological information technologies in China

Shanjun Mao
2020 International Journal of Coal Science & Technology  
the intelligent coal mining.  ...  The history of coalfield geology and mine geology IT applications is over 30 years, which is gaining remarkable achievements.  ...  The three-dimensional dynamic geological model system (1) Data sharing with the geological and surveying thematic graphics processing and collaborative information management system. (2) Constructing dynamic  ... 
doi:10.1007/s40789-020-00340-1 fatcat:gium2unu7febjicfjurtww2lru

Research of Data Mining of Association Pattern Pairs in Multidimensional Structured Database

Zhang Jing, Huang Xiantong, Yang Xinfeng
2011 Energy Procedia  
Secondly, we propose a new data mining problem, the structure of the database to find frequent patterns associated pair.  ...  To effectively address these problems, we developed a series of cutting ability with a strong algorithm.  ...  Carried out in the graphics database is another problem associated with mining mining HSG [11] .  ... 
doi:10.1016/j.egypro.2011.10.347 fatcat:vxchwunkgjfgxpgeqcbforvrm4

Open-Source Tools for Data Mining

Blaz Zupan, Janez Demsar
2008 Clinics in Laboratory Medicine  
But compared with the data mining suites of today, they were awkward, most often providing only command-line interfaces and at best offering some integration with other packages through shell scripting  ...  at that time already well developed and used for data exploration and model inference.  ...  MLCþþ, for instance, was acquired by Silicon Graphics in mid 1990s, and turned into MineSet [8] , at that time the most sophisticated data mining environment with many interesting data and model visualizations  ... 
doi:10.1016/j.cll.2007.10.002 pmid:18194717 fatcat:6x4ad7dr5zg63azyrnznnkkp7m

An Analytical Review of Data Mining Tools

Igiri Chinwe Peace
2015 International Journal of Engineering Research and  
Data mining plays a vital role in the contemporary society and the corporate world as a whole.  ...  More often than not, young researchers face the challenge of making choice of a data mining tool to carry out their research.  ...  On the other hand, the Latticist package uses a graphical interface with lattice graphics to analyze data, and the tools supports data selections annotations, plots, brushing and sub setting among others  ... 
doi:10.17577/ijertv4is040611 fatcat:vg7iuq2ggjdenedjdwdsle47li

A modern vision of simulation modelling in mining and near mining activity

Roman Dychkovskyi, Volodymyr Falshtynskyi, Vladyslav Ruskykh, Edgar Cabana, Oleksandr Kosobokov, V. Bondarenko, I. Kovalevska, R. Lysenko, O. Malova, F. Cawood, M. Hardygora
2018 E3S Web of Conferences  
The main purpose of such research is the simulation reproduction of all technological processors associated with the activity of mining enterprises on the display of the dispatch center.  ...  For this purpose, is used so-called UML-diagrams, which allows to simulate mining and near mining processes. Results of this investigation were included to the Roman  ...  Tool for Wider Society Learning" and Dubrovnik International ESEE Mining School (projects in the frame of EIT Raw Materials).  ... 
doi:10.1051/e3sconf/20186000014 fatcat:5cfwsc6ga5bl3hvpfxbwe6p25a

A Review: Comparative Study Of Diverse Collection Of Data Mining Tools

S. Sarumathi, N. Shanthi, S. Vidhya, M. Sharmila
2014 Zenodo  
Data mining mainly contracts with excessive collection of data that inflicts huge rigorous computational constraints.  ...  Furthermore the major aspire of data mining software is to build a resourceful predictive or descriptive model for handling large amount of information more efficiently and user friendly.  ...  the performance of models graphically and scored new datasets.  ... 
doi:10.5281/zenodo.1094098 fatcat:nf3nvlrzrbd3naavgur6ym2hc4

Revealing daily human activity pattern using process mining approach

Muhammad Rifqi Ma'arif
2017 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)  
Process mining is data-driven approach to infer a graphical representation of any kind of process.  ...  But the problem with this approach is the complexity of knowledge representation which formulated in mathematical model. In turns, a correction by the experts is hardly conducted.  ...  Process Mining The main purpose of process mining is to infer process related information from event logs and represented as a graphical models [7] .  ... 
doi:10.1109/eecsi.2017.8239160 fatcat:l5mbmq6hxbgzbmacu67qkrrqce

Data Mining Methods and Techniques in Higher Education

Shilpa Kulkarni, Dr. Sasikala P
2022 IJARCCE  
This is a strategy for determining how data mining tools affect higher education.  ...  It uses a number of tools and techniques from machine learning, statistics, data mining, and data analysis to analyses data created during teaching and learning.  ...  Rapid Miner also includes a number of metrics for model evaluations, as well as graphics such as Receiver-Operating Curves, to help with model fit evaluation.  ... 
doi:10.17148/ijarcce.2022.11242 fatcat:bzk24f7yqzfmhe7glzepwnegh4
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