Cluster Analysis of Trees for Fauna
Subham Chauhan1, Swarnim Gupta2, Nipun Tank3

1Subham Chauhan, CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Swarnim Gupta, CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Nipun Tank, CSE, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.

Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1896-1899 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6220058719/19©BEIESP
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Abstract: Nowadays, many problems are arising due to large scale deforestation and soil erosion. Instead of leaving the land uncultivated, it can be used in a beneficial way by growing beneficial plants. Different species of plants require different climatic conditions for optimum growth. So, clustering of plants can be beneficial such that plants will get an adequate amount of nutrients. By clustering the plants, the appropriate land can be found in which surplus amount can be grown easily. Different plants need different types of nutrients from the soil and also, the groundwater level of all areas isn’t the same. Hence, there is a need to identify the right area of land for cultivation. This will not only be advantageous for humans and animals but also, it will help improve the environment of the area. Afforestation will help improve the water cycle, reduce soil erosion and other such issues. The report will consist of trees grouped in clusters of different species. This could be done by data mining, and the clustering algorithm is a particular data mining concept. Cluster analysis is a method in which clusters are formed based on common characteristics i.e. elements in the same cluster are analogous to each other than those in other clusters. R language could be used for clustering trees and plants of a particular area. It is free software for statistical computing and graphics.
Keyword: Cluster Analysis, Data mining, Land Utilization, R tool
Scope of the Article: Clustering.