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This paper presents a solution to the approximate k-means clustering problem for very large distributed datasets. Distributed data models have gained popularity in recent years following the efforts of commercial, academic and government organizations, to make data more widely accessible. Due to the sheer volume of available data, in-memory single-core computation quickly becomes infeasible, requiring distributed multi-processing. Our solution achieves comparable clustering performance to other popular clustering algorithms, with improved overall complexity growth while being amenable to distributed processing frameworks such as Map-Reduce. Our solution also maintains certain guarantees regarding data privacy deanonimization.
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Softening Performance's Pitfalls by Integrating Context and Capacity: A Government Competitiveness Framework
Abstract This article argues that government performance is better understood and managed within a broader competitiveness framework. Government competitiveness recursively integrates performance with organizational capacity and context. We illustrate this more holistic view with recent COVID‐19 examples as well as recent scholarship, including some recent PAR publications related to this topic .
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- Award ID(s):
- 1952096
- PAR ID:
- 10465228
- Date Published:
- Journal Name:
- Public Administration Review
- Volume:
- 82
- Issue:
- 5
- ISSN:
- 0033-3352
- Page Range / eLocation ID:
- 887 to 892
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/puar.13434
