The prediction of the occurrence of heart disease in the medical area is an important task. Algorithm of data mining are very helpful in the detection of Cardiovascular disease. In this paper, a survey has been provided for data mining classification techniques, in which health professionals have...
In knowledgediscovery process association rule mining and classification aretwo important techniques of data mining and widely used invarious fields. In order to mine only rules that can be used forprediction, we modified the well known association rule miningalgorithm - Apriori to handle user-...
Principles of Data Mining and Knowledge Discovery, PKDD 2001, Springer (2001), pp. 253-265 CrossrefGoogle Scholar [17] L. Jost Entropy and diversity Oikos, 113 (2) (2006), pp. 363-375 CrossrefView in ScopusGoogle Scholar [18] W.W. Li, Z.Q. Huang, X.Y. Jia, X.Y. Cai Neighbo...
What is the difference between data mining and big data? Who coined the term big data? How did big data start? How long has big data been around? Who created big data? What are the tools for big data? What is the maximum number of 16-bit words that can be stored in a 2^32 ...
Fig. 1. Distribution of plasmid sizes according to relaxase MOB class. The histogram shows the abundance of plasmid size bins. For plasmids encoding a relaxase, the different colors indicate each of the nine MOB classes. Data were obtained from 9894 plasmid sequences retrieved from RefSeq data...
The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying characteristics that could reasonably be drawn from that data using new statistical methods. One data type of p
Such systems support a proactive maintenance strategy by continuously monitoring the components of interest and predicting their failures sufficiently in advance to avoid disruptions during operation. Recent research demonstrated the potential of a comprehensive data mining methodology for building prognostic ...
data is highly required. The great disadvantage of aforementioned methods is that they “assume” that statistical properties of the discovered concept (which model is predicted) are being unchanged. In real situation we could observe so-called concept drift, therefore designing data mining methods, ...
RNNs are a type of ANN in which nodes create a guided graph along a temporal sequence and have an internal memory that allows them to remember important information about the input data [164–166]. As a result, RNNs are good and preferred algorithms for dealing with sequential data and ...
Customer lifetime value (CLV)-base deep learning ensemble model for customer classification and multi-entity matching strategies is provided. In one novel aspect, the customer lifetime value (CLV)-base deep learning model (DNN) uses data mining and an ensemble of the recurrent neural network (RN...