Calculating Adjusted Rank Index using Locality Sensitive Hashing (LSH): A Gaussian Approach

Calculating Adjusted Rank Index using Locality Sensitive Hashing (LSH): A Gaussian Approach

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-51 Number-1
Year of Publication : 2017
Authors : Aritra Banerjee
DOI :  10.14445/22315381/IJETT-V51P207

Citation 

Aritra Banerjee "Calculating Adjusted Rank Index using Locality Sensitive Hashing (LSH): A Gaussian Approach", International Journal of Engineering Trends and Technology (IJETT), V51(1),41-44 September 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Locality Sensitive Hashing (LSH) is a technique which is generally used to reduce the dimensionality of the given data. In this paper, I have used the Gaussian approach to reduce the dimensionality of a given massive dataset. Then used the binary matrix generated and created a neighbourhood graph for the given dataset. From the neighbourhood graph derived we can calculate the Adjusted Rank Index (ARI) value of the given dataset after applying Locality Sensitive Hashing. Since LSH is an Approximate Nearest Neighbour (ANN) calculation we approximately find the nearest neighbours of the given training dataset and check ARI value to see how closely the value approximately is from the actual neighbours present of different classes in the dataset.

Reference
[1] Anand RajaRaman, Jefferey Ullman, “Mining of Massive Datasets”
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[5]https://www.youtube.com/watch?v=Arni-zkqMBA
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Keywords
Locality Sensitive Hashing, Adjusted Rank Index, Gaussian, Approximate Nearest Neighbour.