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What is Hierarchical Clustering

Chapter 10
Handbook of Research on Democratic Strategies and Citizen-Centered E-Government Services
During the iterative process the clusters are formed either by splitting one into two new clusters or by merging two clusters into new one.
Published in Chapter:
E-Government Documents and Data Clustering
Goran Šimić (University of Defense, Serbia)
Copyright: © 2015 |Pages: 28
DOI: 10.4018/978-1-4666-7266-6.ch010
Abstract
This chapter is about documents and data clustering as a process of preparing the information resources stored in the e-government systems for advanced search. These resources are mainly represented as textual data stored as field values in the databases or located as documents in file repositories. Due to their growth in number, search for some specific information takes more time. Different techniques are used for this purpose. Most of them include information retrieval based on a variety of text similarity measures. The cost of such processing depends on preparation of resources for searching. Clustering represents the most commonly used technique for such a purpose, and this fact is the basic motive for this chapter.
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Chapter 18
Data Analytics in Industry 4.0: In the Perspective of Big Data
A data mining method for gathering hierarchical structure when grouping related data via top down or bottom up approach.
Published in Chapter: Data Analytics in Industry 4.0: In the Perspective of Big Data; From: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
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Chapter 61
C-MICRA: A Tool for Clustering Microarray Data
This involves the recursive clustering of data points, which may be agglomerative or divisive. An agglomerative clustering method starts with each case in a separate cluster and then combines the clusters sequentially, reducing the number of clusters at each step until only one cluster is left.?The divisive hierarchical clustering starts with all objects in one cluster and then subdivides them into smaller pieces.
Published in Chapter: C-MICRA: A Tool for Clustering Microarray Data; From: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
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Chapter 13
Digital Transformation and Reimagined Brand Messages for Travelers in the Pandemic: Empirical Investigation on Twitter Data From Cruise Brands
Euclidean distance-based grouping techniques on data points to identify within-group similarities and between-group differences.
Published in Chapter: Digital Transformation and Reimagined Brand Messages for Travelers in the Pandemic: Empirical Investigation on Twitter Data From Cruise Brands; From: Handbook of Research on Smart Management for Digital Transformation
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Chapter 18
Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
The most common approach to clustering. The method proceeds sequentially, producing a nested assignment of objects into clusters. It is typically agglomerative, with cluster sizes increasing as the number of clusters decreases. At each step of the process, a clustering criterion based on a measure of proximity between groups must be computed to decide which groups of objects are to be joined together.
Published in Chapter: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques; From: Handbook of Research on Big Data Clustering and Machine Learning
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Chapter 5
Production of Evidence-Based Informed Consent (EBIC) With Meaning Equivalence Reusable Learning Objects (MERLO): An Application on the Clinical Setting
Data driven statistical method used to place observations into groups (clusters) with a predetermined ordering from top to bottom. Each cluster contains “n” number of observations that are more similar between them than the ones contained in the other clusters.
Published in Chapter: Production of Evidence-Based Informed Consent (EBIC) With Meaning Equivalence Reusable Learning Objects (MERLO): An Application on the Clinical Setting; From: Pedagogy for Conceptual Thinking and Meaning Equivalence: Emerging Research and Opportunities
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Chapter 8
Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques
A method separates different data points to different clusters based on hierarchy and merge different clusters to one.
Published in Chapter: Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 10
Swarm Intelligence in Text Document Clustering
The hierarchical clustering techniques produce a nested sequence of partition, with a single, all-inclusive cluster at the top and single clusters of individual points at the bottom.
Published in Chapter: Swarm Intelligence in Text Document Clustering; From: Handbook of Research on Text and Web Mining Technologies
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Chapter 1
Exploring the Unknown Nature of Data: Cluster Analysis and Applications
Hierarchical clustering (HC) organizes data with a sequence of nested partitions, either from singleton clusters to a cluster including all individuals or vice versa.
Published in Chapter: Exploring the Unknown Nature of Data: Cluster Analysis and Applications; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 3
Public Utility System Evolution: Hierarchical Clustering Pricing Procedure
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Chapter 60
Data Clustering
It is to build a hierarchical decomposition of data in either bottom-up or top-down way. Generally a dendrogram is generated and a user may select to cut it at a certain level to get the clusters.
Published in Chapter: Data Clustering; From: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
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Chapter 45
Cluster Analysis of Gene Expression Data
A clustering method that finds successive clusters using previously established clusters. Hierarchical algorithms can be agglomerative (bottom-up) or divisive (top-down). Agglomerative algorithms begin with each element as a separate cluster and merge them into successively larger clusters. Divisive algorithms begin with the whole set and proceed to divide it into successively smaller clusters.
Published in Chapter: Cluster Analysis of Gene Expression Data; From: Encyclopedia of Artificial Intelligence
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Chapter 2
A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques
A data mining method for gathering hierarchical structure when grouping related data via top down or bottom up approach.
Published in Chapter: A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques; From: Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics
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Chapter 52
Categorization of Data Clustering Techniques
Hierarchical clustering is the process of creating a hierarchical decomposition of a data set.
Published in Chapter: Categorization of Data Clustering Techniques; From: Handbook of Research on Public Information Technology
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Chapter 4
Cluster Analysis in R With Big Data Applications
A clustering technique that iteratively collects or separates data points into clusters using a given linkage method to evaluate the distance between clusters.
Published in Chapter: Cluster Analysis in R With Big Data Applications; From: Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
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