The data mining techniques help researchers to predict the changes in the intensity of volcanos. This paper uses predictive statistical models that can be applied to areas such as seismic activity , the spreading of fire. The basic problem in this class of systems is unobservable dynamics with ...
MOZAFARY V,PAYVANDY P.Application of datamining technique in predicting worsted spun yarnquality[J].Journal of the Textile Institute,2013,105(1):100-108.MOZAFARY V; PAYVANDY P.Application of data mining technique in predicting worsted spun yarn quality.Journal of the Textile Institute.2013.100...
In this data era where data is the new oil, internet data traffic is growing significantly each year [1,2]. With the advent of state of the art technologies on data transmission and processing in the last decade, the internet has witnessed an increase in the intensity and the volume of i...
In data mining, you really need to have real data to discover interesting relationships. So for this paper we will use some real data that I have, although it is not customer data. The data is the HTTP web logs from my Internet site. The structure of the Internet log data is similar ...
Football outsiders almanac 2013: the essential guide to the 2013 NFL and college football seasons. Seattle, WA: CreateSpace; 2013. Google Scholar [25] Schumaker RP, Solieman OK, Chen H. Predictive modeling for sports and gaming. In: Schumaker RP, Solieman OK, Chen H, Sports data mining....
《Data Mining with R》这本书通过实例,并结合R讲解数据挖掘技术。本书的核心理念就是“Learning it by doing”。本书分5章,第一章介绍R和MySql的基本知识,后面4章分别结合4个案例进行讲解。最精刚刚看完第二章,觉得还是学习了一些新的东西,在这里记录一下,作为备忘。
Therefore, we explore the need to learn about the work done in relation to efforts to establish teacher evaluation based on student-supplied data. To this end, this systematic literature review (SLR) aims to identify studies that integrate predictive applications through educational data mining, disc...
the users' influence in SNS,in which different indicators' datasets,different feature selection methods,different proportions of data oversampling and multiple prediction algorithms are included.Our empirical result,which is validated by different performance evaluation indicators,shows that the proposed ...
Here, we train a deep learning classifier to provide an early warning signal for the five local discrete-time bifurcations of codimension-one. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of ...
Data mining techniques refer to extraction techniques that are performed on large sets/volume of data or the big data. Big data mining is used to extract and retrieve desired information or pattern...doi:10.1007/978-981-13-8676-3_59Nayak, Manjushree...