Studies on Identification and Classification of the Best Communities That Ensure the Mastery of Their Expenditure by Using the Threshold of Their Cluster
DOI:
https://doi.org/10.9734/bpi/nvst/v6/3418DKeywords:
Data mining, unsupervised classification, clustering algorithms, threshold, separation distance, categorical data, revenue ratios, Current Cash Flow (CCF), Local Community (LC)Abstract
In the context of the rationalization of expenditure of local communities, we have developed a technique for segmentation of local communities according to their revenue ratios by using the algorithms of data mining, and identifying in the first place, their weight to control expenditure in their cluster. The latter is characterized by a threshold dependent on the number of its elements. In a second level, we built a new reorganization in their classification in order to increase the weight and subsequently to ensure better control and good management of local communities spending. The rationalization of public expenditures will be effective at the national level unless it is established at the regional level.
Published
2021-10-02
How to Cite
Anouar Riad Solh, & Mourad El Belkacemi. (2021). Studies on Identification and Classification of the Best Communities That Ensure the Mastery of Their Expenditure by Using the Threshold of Their Cluster. New Visions in Science and Technology Vol. 6, 98–109. https://doi.org/10.9734/bpi/nvst/v6/3418D
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