Hierarchical and Non-Hierarchical Medoid Clustering Using Asymmetric Similarity Measures

2016 ◽  
Author(s):  
Sadaaki Miyamoto ◽  
Yousuke Kaizu ◽  
Yasunori Endo
Keyword(s):  
Similarity Measures ◽  
Asymmetric Similarity

Directional distributional similarity for lexical inference

2010 ◽  
Vol 16 (4) ◽  
pp. 359-389 ◽  
Author(s):  
LILI KOTLERMAN ◽  
IDO DAGAN ◽  
IDAN SZPEKTOR ◽  
MAAYAN ZHITOMIRSKY-GEFFET
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Similarity Measures ◽  
Average Precision ◽  
Inference Making ◽  
Symmetric Relation ◽  
Word Similarity ◽  
Directional Relations ◽  
Asymmetric Similarity

AbstractDistributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional (asymmetric) similarity measures that aim to quantify distributional feature inclusion. We identify desired properties of such measures for lexical inference, specify a particular measure based on Average Precision that addresses these properties, and demonstrate the empirical benefit of directional measures for two different NLP datasets.

Agglomerative Hierarchical Clustering Without Reversals on Dendrograms Using Asymmetric Similarity Measures

2012 ◽  
Vol 16 (7) ◽  
pp. 807-813
Author(s):  
Satoshi Takumi ◽  
◽  
Sadaaki Miyamoto
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measures ◽  
Real Data ◽  
Agglomerative Hierarchical Clustering ◽  
Average Linkage ◽  
Linkage Methods ◽  
Asymmetric Similarity

Algorithms of agglomerative hierarchical clustering using asymmetric similarity measures are studied. Two different measures between two clusters are proposed, one of which generalizes the average linkage for symmetric similarity measures. Asymmetric dendrogram representation is considered after foregoing studies. It is proved that the proposed linkage methods for asymmetric measures have no reversals in the dendrograms. Examples based on real data show how the methods work.

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

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