Open AccessClustering Analysis using an Unsupervised Machine Learning Method
Author(s) -
Tashfin Ansari,
Almas Siddiqui,
Awasthi G. K
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit12173174
Subject(s) - cluster analysis , computer science , object (grammar) , field (mathematics) , artificial intelligence , unsupervised learning , data mining , machine learning , cluster (spacecraft) , simple (philosophy) , correlation clustering , data stream clustering , cure data clustering algorithm , mathematics , philosophy , epistemology , pure mathematics , programming language
Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.