IMPROVEMENT OF DATA ANALYSIS BASED ON K-MEANS ALGORITHM AND AKMCA
Data analysis is improved using the k-means algorithm and AKMCA. Data mining aims to extract information from a large data set and transform it into a functional structure. Exploratory data analysis and data mining applications rely heavily on clustering. Clustering is grouping a set of objects so that those in the same group (called a cluster) are more similar to those in other groups (clusters). There are various types of cluster models, such as connectivity models, distribution models, centroid models, and density models. Clustering is a technique in data mining in which the set of objects is classified as clusters. Clustering is the most important aspect of data mining. The algorithm makes use of the density number concept. The high-density number point set is extracted from the original data set as a new training set, and the point in the high-density number point set is chosen as the initial cluster centre point. The basic clustering technique and the most widely used algorithm is K-means clustering.
K-Means, a partition-based clustering algorithm, is widely used in many fields due to its efficiency and simplicity. However, it is well known that the K-Means algorithm can produce suboptimal results depending on the initial cluster centre chosen. It is also referred to as Looking for the nearest neighbours. It simply divides the datasets into a specified number of clusters. Numerous efforts have been made to improve the K-means clustering algorithm’s performance. Advanced k-mean clustering algorithm (AKMCA) is used in data analysis to obtain useful knowledge of various optimisation and classification problems that can be used for processing massive amounts of raw and unstructured data. Knowledge discovery provides the tools needed to automate the entire data analysis and error reduction process, where their efficacy is investigated using experimental analysis of various datasets. The detailed experimental analysis and a comparison of proposed work with existing k-means clustering algorithms. Furthermore, it provides a clear and comprehensive understanding of the k-means algorithm and its various research directions.
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