Freshness Detection of Coconut via K-Means

Scalable Kernel k-Means via Centroid Approximation

Although kernel k-means is central for clustering complex data such as images and texts by implicit feature space embedding, its practicality is limited by the quadratic computational complexity. In this paper, we present a novel technique based on scalable centroid approximation that accelerates kernel k-means down to a sub-quadratic complexity. We prove near-optimality of our algorithm, and e...

Topic Model Estimation via K-Means

Traffic Anomaly Detection Using K-Means Clustering

Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. If applied to network monitoring data recorded on a host or in a network, they can be used to detect intrusions, attacks and/or anomalies. This paper gives an introduction to Network Data Mining, i.e. the application of data mining methods to packet and flow data captured in a network,...

Outlier Detection using Improved Genetic K-means

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simul...

Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...