An Adaptive Three-Way Clustering Algorithm for Mixed-Type Data

2018 ◽  
pp. 379-388 ◽  
Author(s):  
Jing Xiong ◽  
Hong Yu
Keyword(s):  
Mixed Type ◽  
Clustering Algorithm ◽  
Type Data

scholarly journals An Improved Kernel Clustering Algorithm for Mixed-Type Data in Network Forensic

2016 ◽  
Vol 10 (1) ◽  
pp. 343-354 ◽  
Author(s):  
Min Ren ◽  
Peiyu Liu ◽  
Zhihao Wang ◽  
Lin Lü
Keyword(s):  
Mixed Type ◽  
Clustering Algorithm ◽  
Kernel Clustering ◽  
Type Data ◽  
Network Forensic

An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood

2017 ◽  
Vol 133 ◽  
pp. 294-313 ◽  
Author(s):  
Shifei Ding ◽  
Mingjing Du ◽  
Tongfeng Sun ◽  
Xiao Xu ◽  
Yu Xue
Keyword(s):  
Mixed Type ◽  
Clustering Algorithm ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Type Data

Unsupervised evolutionary clustering algorithm for mixed type data

2010 ◽  
Author(s):  
Zhi Zheng ◽  
Maoguo Gong ◽  
Jingjing Ma ◽  
Licheng Jiao ◽  
Qiaodi Wu
Keyword(s):  
Mixed Type ◽  
Clustering Algorithm ◽  
Evolutionary Clustering ◽  
Type Data

scholarly journals Applications of Clustering with Mixed Type Data in Life Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Shuang Yin ◽  
Guojun Gan ◽  
Emiliano A. Valdez ◽  
Jeyaraj Vadiveloo
Keyword(s):  
Life Insurance ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Financial Statements ◽  
Life Insurers ◽  
Insurance Portfolio ◽  
Policy Information ◽  
Spatial Attributes ◽  
Type Data ◽  
Significant Mortality

Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to examine and understand how actual death claims differ from what is expected—an early stage of developing a monitoring system crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset using only the insured’s characteristics and policy information without regard to known mortality. This clustering has the feature of efficiently handling categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contained observations of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders’ attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.

scholarly journals Visualizing Inequality in Health and Socioeconomic Wellbeing in the EU: Findings from the SHARE Survey

2020 ◽  
Vol 17 (21) ◽  
pp. 7747
Author(s):  
Aurea Grané ◽  
Irene Albarrán ◽  
Roger Lumley
Keyword(s):  
Physical Health ◽  
Mixed Type ◽  
Clustering Algorithm ◽  
Demographic Variables ◽  
Health And Nutrition ◽  
Composite Indices ◽  
Inequality In Health ◽  
Perception Of Health ◽  
Older Europeans ◽  
The Eu

The main objective of this paper is to visualize profiles of older Europeans to better understand differing levels of dependency across Europe. Data comes from wave 6 of the Survey of Health, Ageing and Retirement in Europe (SHARE), carried out in 18 countries and representing over 124 million aged individuals in Europe. Using the information of around 30 mixed-type variables, we design four composite indices of wellbeing for each respondent: self-perception of health, physical health and nutrition, mental agility, and level of dependency. Next, by implementing the k-prototypes clustering algorithm, profiles are created by combining those indices with a collection of socio-economic and demographic variables about the respondents. Five profiles are established that segment the dataset into the least to the most individuals at risk of health and socio-economic wellbeing. The methodology we propose is wide enough to be extended to other surveys or disciplines.

Growing Self-Organizing Map with cross insert for mixed-type data clustering

2012 ◽  
Vol 12 (9) ◽  
pp. 2856-2866 ◽  
Author(s):  
Wei-Shen Tai ◽  
Chung-Chian Hsu
Keyword(s):  
Data Clustering ◽  
Mixed Type ◽  
Self Organizing Map ◽  
Type Data ◽  
Self Organizing
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