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Data Mining of Inputs: Analysing Magnitude and Functional Measures

Tamás D. Gedeon
1997 International Journal of Neural Systems  
This paper examines the use of weight matrix analysis techniques and functional measures using two real (and hence noisy) data sets.  ...  The second part of this paper examines the use of measures to determine the functional contribution of inputs to outputs.  ...  The experimental work demonstrated that the functional measures, particularly based on the analysis of the network and not just the data, produce better indicators of the significance of particular inputs  ... 
doi:10.1142/s0129065797000227 fatcat:tmwxi4jmjbesbprxgkxr5gylhm

Automated trend analysis of proteomics data using an intelligent data mining architecture

2006 Expert systems with applications  
We present an intelligent data mining architecture that incorporates both data-driven and goal-driven strategies and is able to accommodate the spatial and temporal elements of the dataset under analysis  ...  Using a data mining technique to detect variance within the data before classification offers performance advantages over other statistical variance techniques in the order of between 16 and 46%. q  ...  Acknowledgements The authors would like to acknowledge the support of EPSRC (grant GR/P01205), NonLinear Dynamics Ltd and Biswarup Mukhopadhyay of the Virginia Bioinformatics Institute.  ... 
doi:10.1016/j.eswa.2005.09.047 fatcat:n3tzyhtwsfdnxkgqn6qp5uvxkm

Effective Data Mining for Proper Mining Classification Using Neural Networks

Gaurab Tewary
2015 International Journal of Data Mining & Knowledge Management Process  
This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.  ...  With the development of database, the data volume stored in database increases rapidly and in the large amounts of data much important information is hidden.  ...  The answer is Data mining [1, 2] Techniques/Functionalities of Data Mining There are two fundamental goals of data mining: prediction and description.  ... 
doi:10.5121/ijdkp.2015.5206 fatcat:mn3orynfizatjdhfdzcocon7pm

Utility based data mining for time series analysis

Sven F. Crone, Stefan Lessmann, Robert Stahlbock
2005 Proceedings of the 1st international workshop on Utility-based data mining - UBDM '05  
Conversely, data mining methods for regression and time series analysis generally disregard economic utility and apply simple accuracy measures.  ...  In corporate data mining applications, cost-sensitive learning is firmly established for predictive classification algorithms.  ...  Similarly, for the predictive data mining problems of regression and time series analysis [10, 11] the costs arising from invalid point prediction of the true realisation increase with the magnitude  ... 
doi:10.1145/1089827.1089835 fatcat:lmefsuxfencwpawuwwy4zbk6km

Analysis of Stock Market using Data Mining Techniques

Ashmita Phuyal, Aditi Pokharel, Nilima Dahal, Sushil Shrestha
2021 Zenodo  
In this paper we present a data mining and machine learning aided approach to evaluate the equity's future price over the long term.  ...  Due to the presence of non-linear data sets and dynamic nature, there is an increasing demand in analysis of the market and prediction of future stock trends.  ...  In this regression approach, linear predictor functions are used to model the correlations and the unknown parameters of the functions are evaluated by the data.  ... 
doi:10.5281/zenodo.5075321 fatcat:ri423sagpfbl7nnxslaloi7qnm

Real-time data mining of massive data streams from synoptic sky surveys

S.G. Djorgovski, M.J. Graham, C. Donalek, A.A. Mahabal, A.J. Drake, M. Turmon, T. Fuchs
2016 Future generations computer systems  
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition  ...  Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets.  ...  Some of this work was assisted by the Caltech students Nihar Sharma, Yutong Chen, Alex Ball, Victor Duan, Allison Maker, and others, supported by the Caltech SURF program.  ... 
doi:10.1016/j.future.2015.10.013 fatcat:2dv4dtnlsvaqdbl6m35h5vl4ge

The application of data mining techniques for the regionalisation of hydrological variables

M. J. Hall, A. W. Minns, A. K. M. Ashrafuzzaman
2002 Hydrology and Earth System Sciences  
However, hydrological regionalisation can also be regarded as a problem in data mining -a search for useful knowledge and models embedded within large data sets.  ...  This approach has been applied to three data sets from the south-west of England and Wales; to England, Wales and Scotland (EWS); and to the islands of Java and Sumatra in Indonesia.  ...  In contrast, when the data sets were analysed using a data mining technique involving unsupervised learning, three classes of catchment were identified for both Indonesia and south-west England and Wales  ... 
doi:10.5194/hess-6-685-2002 fatcat:3jkgrqtrvvbbnbbdhi2g7ltmh4

Using Data Mining for Static Code Analysis of C [chapter]

Hannes Tribus, Irene Morrigl, Stefan Axelsson
2012 Lecture Notes in Computer Science  
Due to the general interest in code quality and the availability of large open source code bases as test and development data, we believe this problem should be of interest to the larger data mining community  ...  In this work we extend our previous approach and investigate a new way of doing feature selection and test the suitability of many different learning algorithms.  ...  Static Analysis by Data Mining We now arrive at the problems of how to transform static source code to a suitable format for data mining, which data to use to train the classifier, and which classifier  ... 
doi:10.1007/978-3-642-35527-1_50 fatcat:zmmomof7bvbvxlgv425t5dv7w4

Data mining techniques for improving the reliability of system identification

S. Saitta, B. Raphael, I.F.C. Smith
2005 Advanced Engineering Informatics  
Data mining techniques bring out model characteristics that are important.  ...  A system identification methodology that makes use of data mining techniques to improve the reliability of identification is presented in this paper.  ...  Users input measurement data and specify a set of modelling assumptions. The model selection process identifies a set of candidate models whose predictions are close to measurements.  ... 
doi:10.1016/j.aei.2005.07.005 fatcat:kvmlvjtluncyhmir3hjnvtqaqe

Data mining of enzymes using specific peptides

Uri Weingart, Yair Lavi, David Horn
2009 BMC Bioinformatics  
We devise the Data Mining of Enzymes (DME) methodology that allows for searching SPs on arbitrary proteins, determining from its sequence whether a protein is an enzyme and what the enzyme's EC classification  ...  The outcome of these analyses can be characterized by the enzymatic profile of the metagenomes, describing the relative numbers of enzymes observed for different EC categories.  ...  Acknowledgements We thank Uri Gophna and Eytan Ruppin for helpful conversations. This study was supported in part by fellowships granted to UW and YL by the Edmond J.  ... 
doi:10.1186/1471-2105-10-446 pmid:20034383 pmcid:PMC2811123 fatcat:t36fjyw3xfapplsvqe66lc7lqm

Applications of Data Mining to Diagnosis and Control of Manufacturing Processes [chapter]

Marcin Perzyk, Robert Biernacki, Andrzej Kochanski, Jacek Kozlowski, Artur Soroczynski
2011 Knowledge-Oriented Applications in Data Mining  
The magnitude of the scatter of the significance factor of a given input resulting from the other inputs' levels can be a measure of the possible interactions with the other input variables.  ...  The methods of finding the relative significances of input variables for small data sets, both in regression and classification type tasks, require further analyses and improvements.  ... 
doi:10.5772/13282 fatcat:xqp45iiyofa5pixdsz7v6yfodu

Summarizing and Mining Skewed Data Streams [chapter]

Graham Cormode, S. Muthukrishnan
2005 Proceedings of the 2005 SIAM International Conference on Data Mining  
Many applications generate massive data streams. Summarizing such massive data requires fast, small space algorithms to support post-hoc queries and mining.  ...  We support our theoretical results with an experimental study over a large variety of real and synthetic data. We show that significant skew is present in both textual and telecommunication data.  ...  Acknowledgments We thank Yinmeng Zhang for some useful discussions, and the referees for their suggestions.  ... 
doi:10.1137/1.9781611972757.5 dblp:conf/sdm/CormodeM05 fatcat:uo3u27k4avfbrdtsjjzgiadrvy


2010 International Journal of Modern Physics D  
We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field.  ...  Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results.  ...  This research has made use of the SAO/NASA Astrophysics Data System.  ... 
doi:10.1142/s0218271810017160 fatcat:qd442usdmfgalbomkkiyvwzsfu

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Pushan Dutta, O. Mishra, Mrinal Naskar
2013 Open Geosciences  
A series of statistical tests based on multi-dimensional rigorous statistical studies, inter-event distance analyses, and statistical time analyses have been used to obtain correlation dimensions.  ...  We propose a three-layer feed forward neural network model to identify factors, with the actual occurrence of the maximum earthquake level M as input and target vectors in Himalayan basin area.  ...  of the seismic process.  ... 
doi:10.2478/s13533-012-0127-6 fatcat:7awpuubopnesjoditrlknibj2i

Applications of Data Mining in Correlating Stock Data and Building Recommender Systems

Sharang Bhat
2015 International Journal of Computer Applications  
The proposed research paper and eventual system, aims at providing a platform for data entry of large data sets of stock data, a set of input variables (which can be two or more) upon which various clustering  ...  General Terms Data Mining Applications in chart pattern recognition and building recommender systems.  ...  The k-medoids algorithms uses partitioning (breaking the dataset up into groups) and attempts to minimize the objective function which in this case is the distance measure between the data points in the  ... 
doi:10.5120/19948-1756 fatcat:efn5tyjrdvg77i7x5cpwwn6fiq
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