Using unlabeled data mining to detect customer perceptions of undefined commodity problems

2021 ◽  
Vol 27 (3) ◽  
pp. 209
Author(s):  
Linbo Wang ◽  
Fan Zhang ◽  
Yiqiong Wu ◽  
Qing Zhu ◽  
Shan Liu
Keyword(s):  
Data Mining ◽  
Unlabeled Data ◽  
Customer Perceptions

Using unlabeled data mining to detect customer perceptions of undefined commodity problems

2021 ◽  
Vol 27 (3) ◽  
pp. 209
Author(s):  
Yiqiong Wu ◽  
Qing Zhu ◽  
Shan Liu ◽  
Fan Zhang ◽  
Linbo Wang
Keyword(s):  
Data Mining ◽  
Unlabeled Data ◽  
Customer Perceptions

Active Learning Based on Diversity Maximization

2013 ◽  
Vol 347-350 ◽  
pp. 2548-2552 ◽  
Author(s):  
Yong Cheng Wu
Keyword(s):  
Data Mining ◽  
Active Learning ◽  
Comparative Studies ◽  
Unlabeled Data ◽  
Learning Approach ◽  
Learning Methods ◽  
Training Examples ◽  
Testing Algorithm

In many practical data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, as one type of the paradigms for addressing the problem of combining labeled and unlabeled data to boost the performance, active learning has attracted much attention. In this paper, we propose a new active learning approach based on diversity maximization. Different from the well-known co-testing algorithm, our method does not require two different views. The comparative studies with other active learning methods demonstrate the effectiveness of the proposed approach.

scholarly journals New algorithm for clustering unlabeled big data

2021 ◽  
Vol 24 (2) ◽  
pp. 1054
Author(s):  
Marwan B. Mohammed ◽  
Wafaa AL-Hameed
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Unlabeled Data ◽  
New Techniques ◽  
Clustering Techniques ◽  
Analysis Techniques ◽  
Hierarchical Clustering Algorithm

The clustering analysis techniques play an important role in the area of data mining. Although from existence several clustering techniques. However, it still to their tries to improve the clustering process efficiently or propose new techniques seeks to allocate objects into clusters so that two objects in the same cluster are more similar than two objects in different clusters and careful not to duplicate the same objects in different groups with the ability to cover all data as much as possible. This paper presents two directions. The first is to propose a new algorithm that coined a name (MB Algorithm) to collect unlabeled data and put them into appropriate groups. The second is the creation of a lexical sequence sentence (LCS) based on similar semantic sentences which are different from the traditional lexical word chain (LCW) based on words. The results showed that the performance of the MB algorithm has generally outperformed the two algorithms the hierarchical clustering algorithm and the K-mean algorithm.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Sign in / Sign up

Export Citation Format

Share Document