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Carson Murray

dblp:85/1393 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
clustering
0.112005
Spatial Clustering of Chimpanzee Locations for Neighborhood Identification · ICDM 2005
Data mining › clustering
spatial clustering
0.112005
Spatial Clustering of Chimpanzee Locations for Neighborhood Identification · ICDM 2005
Environmental and earth informatics › ecology
behavioral ecology
0.012005
Spatial Clustering of Chimpanzee Locations for Neighborhood Identification · ICDM 2005

Methods — techniques the papers use, named apart from their topics

ripley's k-function · 0.1
YearPublicationVenuePosition
2005 Spatial Clustering of Chimpanzee Locations for Neighborhood Identification
abstract
Since 1960, the chimpanzees (Pan troglodytes) of Gombe National Park, Tanzania, have been studied by behavioral ecologists, including Jane Goodall. Data have been collected for more than 40 years and are being analyzed by researchers in order to increase our understanding of the social structure of chimpanzees. In this paper, we consider the following question of interest to behavioral ecologists - "Does clustering exist among female chimpanzees in terms of their spatial locations ?" The analysis of this question will help behavioral ecologists to learn about the space use and the social interactions between female chimpanzees. The data collected for this analysis are marked spatial point patterns over the park. Current spatial clustering methods lack the ability to handle such marked point patterns directly. This paper presents a novel application of spatial point pattern analysis and data mining techniques to the ecological problem of clustering female chimpanzees. We found that Ripley's K-function provides a powerful statistical tool for evaluating clustering behavior among spatial point patterns. We then proposed two clustering approaches for marked point patterns using the K-function. Experimental results using the proposed clustering methods provide significant insight into the dynamics of female chimpanzee space use and into the overall social stucture of the species. In addition, the proposed methods can be extended to also include temporal information.
Sandeep Mane, Carson Murray, Shashi Shekhar 0001, Jaideep Srivastava, Anne Pusey
ICDM2