A Machine Learning approach of Ecological Modeling: A New method to find Similarity Index
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In many scientific research, it is often imperative to determine whether pairs of entities have similarities in themselves or not. There are standard approaches to this problem, such as Jaccard, Sorensen Dice, and Simpson. Recently, a better index for the analysis of cooccurrence and similarity was developed and it reversed all the results obtained by standard indices and supported theoretical predictions. In this paper, we propose a new method of similarity using MLE, PCA, LDA, and clustering. Our index depends strongly on the data before introducing randomness in prevalence. Then we propose a new method of randomization which changed the whole pattern of the results. Before randomization, it was strongly dependent o the prevalence and hence was following the pattern of the Jaccard index. So, we introduce the new randomization technique, and hence the whole results reversed and followed that of alpha. Also, we will show some limitations of alpha which we try to resolve through different pathways.
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