Two-stage aggregation paradigm for HFLTS possibility distributions: A hierarchical clustering perspective

AbstractIdentifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.

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Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya

PLoS ONE ◽

10.1371/journal.pone.0243208 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0243208

Author(s):

Leacky Muchene ◽

Wende Safari

Keyword(s):

Hierarchical Clustering ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Topic Modelling ◽

Two Stage ◽

Scientific Publications ◽

Statistical Tool ◽

Second Stage ◽

The University ◽

Dirichlet Allocation

Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natural language processing and text mining domains. Topic modelling with Latent Dirichlet Allocation is one such statistical tool that has been successfully applied to synthesize collections of legal, biomedical documents and journalistic topics. We applied a novel two-stage topic modelling approach and illustrated the methodology with data from a collection of published abstracts from the University of Nairobi, Kenya. In the first stage, topic modelling with Latent Dirichlet Allocation was applied to derive the per-document topic probabilities. To more succinctly present the topics, in the second stage, hierarchical clustering with Hellinger distance was applied to derive the final clusters of topics. The analysis showed that dominant research themes in the university include: HIV and malaria research, research on agricultural and veterinary services as well as cross-cutting themes in humanities and social sciences. Further, the use of hierarchical clustering in the second stage reduces the discovered latent topics to clusters of homogeneous topics.

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K-means clustering for the aggregation of HFLTS possibility distributions: N-two-stage algorithmic paradigm

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107230 ◽

2021 ◽

pp. 107230

Author(s):

Zhen-Song Chen ◽

Xuan Zhang ◽

Witold Pedrycz ◽

Xian-Jia Wang ◽

Kwai-Sang Chin ◽

...

Keyword(s):

Two Stage ◽

Possibility Distributions

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A two-stage novel approach using centre ordering of vectors on agglomerative hierarchical clustering for manufacturing cell formation

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405417699014 ◽

2017 ◽

Vol 232 (14) ◽

pp. 2651-2662 ◽

Cited By ~ 1

Author(s):

Samiran Bera ◽

Manojit Chattopadhyay ◽

Pranab K Dan

Keyword(s):

Hierarchical Clustering ◽

Cell Formation ◽

Agglomerative Hierarchical Clustering ◽

Two Stage ◽

Manufacturing Cell ◽

Novel Approach

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Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

1989 ◽

Vol 47 ◽

pp. 368-369

Author(s):

Sengshiu Chung ◽

Peggy Cebe

Keyword(s):

Single Crystals ◽

High Performance ◽

Dilute Solution ◽

Two Stage ◽

Annealing Behavior ◽

High Performance Polymers ◽

Seeding Method ◽

Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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