Application of Data Mining in Library-Based Personalized Learning

this paper expounds to mine up data with the DBSCAN algorithm in order to help teachers and students find which books they expect in the sea of library. In the first place, the model that DBSCAN algorithm applies in library data miner is proposed, followed by the DBSCAN algorithm improved on demands. In the end, an experiment is cited herein to validate this algorithm. The results show that the book price and the inventory level in the library produce a less impact on the resultant aggregation than the classification of books and the frequency of book borrowings. Library procurers should therefore purchase and subscribe data based on the results from cluster analysis thereby to improve hierarchies and structure distribution of library resources, forging on the library resources to be more scientific and reasonable, while it is also conducive to arousing readers' borrowing interest.

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Research on Classification of Primary Liver Cancer Syndrome Based on Data Mining Technology

Journal of Healthcare Engineering ◽

10.1155/2022/2629509 ◽

2022 ◽

Vol 2022 ◽

pp. 1-14

Author(s):

Jiwei Fang ◽

Jianfeng Li

Keyword(s):

Data Mining ◽

Cluster Analysis ◽

Liver Cancer ◽

Primary Liver Cancer ◽

Hierarchical Cluster ◽

Cancer Syndrome ◽

Mining Technology ◽

Computer Data ◽

Cancer Syndromes

This study is based on the analysis of the status quo of the research on liver cancer syndromes, starting with the clinical objective and true four-diagnosis information of TCM inpatients with primary liver cancer, using computer data mining technology to analyze and summarize the syndrome rules from the bottom to the top. Let the data itself show the essence of liver cancer syndrome. First, with the help of hierarchical cluster analysis, we can understand the general characteristics through the rough preliminary classification of the four-diagnosis information of liver cancer patients. Then, with the help of the emerging and mature hidden structure model analysis in recent years, through data modeling, the classification of common syndromes of liver cancer and the corresponding relationship with the four-diagnosis information are comprehensively analyzed. Finally, considering the inherent shortcomings of implicit structure and hierarchical clustering based on the assumption that there is a unique one-to-one correspondence between the four diagnostic information factors and the class (or hidden class) when classifying, we plan to use factor analysis and joint cluster analysis, as supplementary means to further explore the classification of liver cancer syndromes and the corresponding relationship with the four-diagnosis information.

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Neurophysiologic classification of Wilson's disease by using cluster analysis

Klinische Neurophysiologie ◽

10.1055/s-2006-939174 ◽

2006 ◽

Vol 37 (01) ◽

Author(s):

W Hermann ◽

T Villmann ◽

HJ Kühn ◽

P Baum ◽

G Reichel ◽

...

Keyword(s):

Cluster Analysis ◽

Wilson’S Disease ◽

Wilson's Disease

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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A Classification of 148 U.S. Maize Inbreds: II. Validation of Cluster Analysis Based on RFLPs

Crop Science ◽

10.2135/cropsci1994.0011183x003400040006x ◽

1994 ◽

Vol 34 (4) ◽

pp. 852-865 ◽

Cited By ~ 10

Author(s):

Rita Hogan Mumm ◽

Lawrence J. Hubert ◽

J. W. Dudley

Keyword(s):

Cluster Analysis ◽

Maize Inbreds

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Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

Proceedings of the Mavlyutov Institute of Mechanics ◽

10.21662/uim2011.1.019 ◽

2011 ◽

Vol 8 (1) ◽

pp. 201-210

Author(s):

R.M. Bogdanov

Keyword(s):

Cluster Analysis ◽

Defect Density ◽

Density Variation ◽

Distance Functions ◽

Oil Pipeline ◽

Oil Pipelines ◽

Classification Of Images ◽

Main Oil Pipeline ◽

Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

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Classification of Deep Sea Sound Speed Profiles in the Northwest Pacific Based on Cluster Analysis

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/734/1/012017 ◽

2021 ◽

Vol 734 (1) ◽

pp. 012017

Author(s):

Yu Zhang ◽

Hua Wang ◽

Xiangjun Yu ◽

Qinghong LI

Keyword(s):

Cluster Analysis ◽

Deep Sea ◽

Sound Speed ◽

Northwest Pacific

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Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

Diagnostics ◽

10.3390/diagnostics10030162 ◽

2020 ◽

Vol 10 (3) ◽

pp. 162 ◽

Cited By ~ 4

Author(s):

Julieta G. Rodríguez-Ruiz ◽

Carlos E. Galván-Tejada ◽

Laura A. Zanella-Calzada ◽

José M. Celaya-Padilla ◽

Jorge I. Galván-Tejada ◽

...

Keyword(s):

Data Mining ◽

Motor Activity ◽

Random Forest Classifier ◽

Machine Learning Algorithms ◽

World Population ◽

Activity Data ◽

The World ◽

Depressive Episodes ◽

Full Day

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

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