Building Fuzzy Thematic Clusters and Mapping Them to Higher Ranks in a Taxonomy

Research output: Contribution to journalArticle

Abstract

We present a novel methodology for the analysis of activities engaged in an organization such as the research conducted in a University department by mapping them to a related hierarchical taxonomy such as Classification of Computer Subjects by ACM (ACM-CCS). We start by collecting data of activities of the individual components of the organization and present them as the components fuzzy membership profiles over the subjects of the taxonomy. Our method generalizes the profiles in two steps. First step finds fuzzy clusters of taxonomy subjects according to the working of the organization. Second, each cluster is mapped to higher ranks of the taxonomy in a parsimonious way. Each of the steps is formalized and solved in a novel way. We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that involves a number of model-based stopping conditions, in contrast to other methods. As the found clusters are not necessarily consistent with the taxonomy, each is considered as a query set. To lift a query set to higher ranks of the taxonomy, we develop an original recursive algorithm for minimizing a penalty function that involves 'head subjects' on the higher ranks of the taxonomy together with their 'gaps' and 'offshoots'. The method is illustrated by applying it to real-world data.
Original languageUnknown
Pages (from-to)257-275
JournalInternational Journal of Software and Informatics
Volume4
Issue number3
Publication statusPublished - 1 Jan 2010

Cite this

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title = "Building Fuzzy Thematic Clusters and Mapping Them to Higher Ranks in a Taxonomy",
abstract = "We present a novel methodology for the analysis of activities engaged in an organization such as the research conducted in a University department by mapping them to a related hierarchical taxonomy such as Classification of Computer Subjects by ACM (ACM-CCS). We start by collecting data of activities of the individual components of the organization and present them as the components fuzzy membership profiles over the subjects of the taxonomy. Our method generalizes the profiles in two steps. First step finds fuzzy clusters of taxonomy subjects according to the working of the organization. Second, each cluster is mapped to higher ranks of the taxonomy in a parsimonious way. Each of the steps is formalized and solved in a novel way. We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that involves a number of model-based stopping conditions, in contrast to other methods. As the found clusters are not necessarily consistent with the taxonomy, each is considered as a query set. To lift a query set to higher ranks of the taxonomy, we develop an original recursive algorithm for minimizing a penalty function that involves 'head subjects' on the higher ranks of the taxonomy together with their 'gaps' and 'offshoots'. The method is illustrated by applying it to real-world data.",
author = "Almeida, {Susana Maria dos Santos Nascimento M. de}",
year = "2010",
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T1 - Building Fuzzy Thematic Clusters and Mapping Them to Higher Ranks in a Taxonomy

AU - Almeida, Susana Maria dos Santos Nascimento M. de

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N2 - We present a novel methodology for the analysis of activities engaged in an organization such as the research conducted in a University department by mapping them to a related hierarchical taxonomy such as Classification of Computer Subjects by ACM (ACM-CCS). We start by collecting data of activities of the individual components of the organization and present them as the components fuzzy membership profiles over the subjects of the taxonomy. Our method generalizes the profiles in two steps. First step finds fuzzy clusters of taxonomy subjects according to the working of the organization. Second, each cluster is mapped to higher ranks of the taxonomy in a parsimonious way. Each of the steps is formalized and solved in a novel way. We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that involves a number of model-based stopping conditions, in contrast to other methods. As the found clusters are not necessarily consistent with the taxonomy, each is considered as a query set. To lift a query set to higher ranks of the taxonomy, we develop an original recursive algorithm for minimizing a penalty function that involves 'head subjects' on the higher ranks of the taxonomy together with their 'gaps' and 'offshoots'. The method is illustrated by applying it to real-world data.

AB - We present a novel methodology for the analysis of activities engaged in an organization such as the research conducted in a University department by mapping them to a related hierarchical taxonomy such as Classification of Computer Subjects by ACM (ACM-CCS). We start by collecting data of activities of the individual components of the organization and present them as the components fuzzy membership profiles over the subjects of the taxonomy. Our method generalizes the profiles in two steps. First step finds fuzzy clusters of taxonomy subjects according to the working of the organization. Second, each cluster is mapped to higher ranks of the taxonomy in a parsimonious way. Each of the steps is formalized and solved in a novel way. We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering method that involves a number of model-based stopping conditions, in contrast to other methods. As the found clusters are not necessarily consistent with the taxonomy, each is considered as a query set. To lift a query set to higher ranks of the taxonomy, we develop an original recursive algorithm for minimizing a penalty function that involves 'head subjects' on the higher ranks of the taxonomy together with their 'gaps' and 'offshoots'. The method is illustrated by applying it to real-world data.

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