scholarly journals An information mining method for deriving weights from an interval comparison matrix

2009 ◽  
Vol 50 (3-4) ◽  
pp. 393-400 ◽  
Author(s):  
JiBin Lan ◽  
Jian Lin ◽  
LiJuan Cao
Keyword(s):  
Mining Method ◽  
Information Mining ◽  
Comparison Matrix ◽  
Interval Comparison

scholarly journals Two Approximation Models of Fuzzy Weight Vector from a Comparison Matrix

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Tomoe Entani
Keyword(s):  
Analytic Hierarchy Process ◽  
Weight Vector ◽  
Analytic Hierarchy ◽  
Lower Approximation ◽  
Comparison Matrix ◽  
Fuzzy Weight ◽  
Interval Weight ◽  
Hierarchy Process ◽  
Interval Comparison ◽  
Approximation Models

In this study, our uncertain judgment on multiple items is denoted as a fuzzy weight vector. Its membership function is estimated from more than one interval weight vector. The interval weight vector is obtained from a crisp/interval comparison matrix by Interval Analytic Hierarchy Process (AHP). We redefine it as a closure of the crisp weight vectors which approximate the comparison matrix. The intuitively given comparison matrix is often imperfect so that there could be various approaches to approximate it. We propose two of them: upper and lower approximation models. The former is based on weight possibility and the weight vector with it includes the comparison matrix. The latter is based on comparison possibility and the comparison matrix with it includes the weight vector.

scholarly journals Applying Lexical Link Analysis to Discover Insights from Public Information on COVID-19

2020 ◽  
Author(s):  
Ying Zhao ◽  
Charles C. Zhou
Keyword(s):  
Open Data ◽  
Public Information ◽  
Link Analysis ◽  
Data Sets ◽  
Large Set ◽  
Mining Method ◽  
Economic Activities ◽  
Data Set ◽  
Information Mining ◽  
Unique Information

SARS-Cov-2, the deadly and novel virus, which has caused a worldwide pandemic and drastic loss of human lives and economic activities. An open data set called the COVID-19 Open Research Dataset or CORD-19 contains large set full text scientific literature on SARS-CoV-2. The Next Strain consists of a database of SARS-CoV-2 viral genomes from since 12/3/2019. We applied an unique information mining method named lexical link analysis (LLA) to answer the call to action and help the science community answer high-priority scientific questions related to SARS-CoV-2. We first text-mined the CORD-19. We also data-mined the next strain database. Finally, we linked two databases. The linked databases and information can be used to discover the insights and help the research community to address high-priority questions related to the SARS-CoV-2’s genetics, tests, and prevention.Significance StatementIn this paper, we show how to apply an unique information mining method lexical link analysis (LLA) to link unstructured (CORD-19) and structured (Next Strain) data sets to relevant publications, integrate text and data mining into a single platform to discover the insights that can be visualized, and validated to answer the high-priority questions of genetics, incubation, treatment, symptoms, and prevention of COVID-19.

Key Data Optimization Mining in Massive Student Database Model

2014 ◽  
Vol 687-691 ◽  
pp. 1466-1469
Author(s):  
Zhen Chao Wang
Keyword(s):  
Data Mining ◽  
Traditional Method ◽  
Experimental Results ◽  
Redundant Information ◽  
Mining Method ◽  
Information Mining ◽  
Database Model ◽  
Data Mining Method ◽  
Mining Model ◽  
Effective Cluster

In the process of massive student data mining using traditional method, special words and related characteristics were used as mining objects. The concealment and feature of deliberately camouflaged of information made it is difficult for mining model to form an effective cluster centers, which reduced the accuracy of information mining. Hence an optimized data mining method was proposed. According to the degree of generalization and fuzziness of the feature words of student, the threshold of mining information was set, which avoided the effects of redundant information, thus the efficiency of mining was improved. The experimental results showed that using the improved algorithm to perform information mining in massive student database could effectively improve mining efficiency.

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