Hashing And Clustering Based Novelty Detection
|International Journal of Computer Science and Engineering|
|© 2019 by SSRG - IJCSE Journal|
|Volume 6 Issue 6|
|Year of Publication : 2019|
|Authors : Pooja Goyal, Sushil Kumar, Komal Kumar Bhatia|
How to Cite?
Pooja Goyal, Sushil Kumar, Komal Kumar Bhatia, "Hashing And Clustering Based Novelty Detection," SSRG International Journal of Computer Science and Engineering , vol. 6, no. 6, pp. 1-9, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I6P101
Novelty detection is a method of identifying new data from the incoming stream of documents. Many technologies for novelty detection are available at present. To enhance the performance of novelty detection, we present another method for novelty detection. In this work, we organize a new structure for novelty detection using hashing and clustering. This method consists of two stages. The first phase consists of dividing sentences, stopwords removal, text preprocessing, converting the N-grams into hashes from adaptations of documents. The second phase consists of clustering and novelty detection based on a fixed threshold. This method is useful for determining the high number of novel documents by detecting new information in data. This method is useful for finding excess in documents and also providing relevant documents against the query. The main aim of this work is to find appropriate and redundant free documents. The goal is to provide information to the user as fast as feasible.
Clustering, Hashing, Novelty Detection, Plagiarism.
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