A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Filters
Data Mining of Inputs: Analysing Magnitude and Functional Measures
1997
International Journal of Neural Systems
Preserved Fulltext
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
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2015
International Journal of Data Mining & Knowledge Management Process
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2005
Proceedings of the 1st international workshop on Utility-based data mining - UBDM '05
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
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
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2016
Future generations computer systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2002
Hydrology and Earth System Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
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]
2012
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2005
Advanced Engineering Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2009
BMC Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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]
2011
Knowledge-Oriented Applications in Data Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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]
2005
Proceedings of the 2005 SIAM International Conference on Data Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is application/pdf.
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
DATA MINING AND MACHINE LEARNING IN ASTRONOMY
2010
International Journal of Modern Physics D
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2009 pre-print arXiv:0906.2173v1 fatcat:bvhko3zrrbe6lowacyiiev2v6eOther Versions
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
2013
Open Geosciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2015
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 55,685 results