Very large databases are required to store massive amounts of data that are continuously
inserted and queried. Analyzing huge data sets and extracting valuable pattern in many …

This paper presents a data clustering approach using modified K-Means algorithm based on
the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm …

With the ever-increasing size of data, clustering of large dimensional databases poses a
demanding task that should satisfy both the requirements of the computation efficiency and …

The k-Means clustering algorithm is an old algorithm that has been intensely researched
owing to its ease and simplicity of implementation. Clustering algorithm has a broad …

Problem statement: Clustering has a number of techniques that have been developed in
statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates …

The EU policies concerning renewable energy systems have put forward a fixed goal of
supplying 20 per cent of the European energy demands from renewable energy systems by …

The algorithm proposed in this paper enhances the K Strange points clustering algorithm by
selecting the first of unchanging K strange points as the minimum of the dataset and then …

With the rapid growth of online property rental and sale platforms, the prevalence of fake real
estate listings has become a significant concern. These deceptive listings waste time and …

The classical K-Means clustering algorithm yields means which can be called the final
unchanging or fixed means around which all other points in the dataset get clustered. This is …

Advances made to the traditional clustering algorithms solves the various problems such as
curse of dimensionality and sparsity of data for multiple attributes. The traditional HK …