Due to the exponential growth of hardware technology particularly in the field of electronic data storage media and processing such data, has raised serious issues related in order to protect the individual privacy like ethical, philosophical and legal. Data mining techniques are employed to ensure the privacy. Privacy Preserving Data Mining (PPDM) techniques aim at protecting the sensitive dat...

Feature Selection in large multi-dimensional data sets is becoming increasingly important for several real world applications. One such application, used by network administrators, is Network Intrusion Detection. The major problem with anomaly based intrusion detection systems is high number of false positives. Motivated by such a requirement, we propose sv(M)kmeans: a two step hybrid feature s...

Since it was first proposed, it is amazing to notice how KMeans algorithm has survive over the years. It has been one among the well known algorithms for data clustering in the field of data mining. Day in and day out new algorithms are evolving for data clustering purposes but none can be as fast and accurate as the K-Means algorithm. But in spite of its huge speed, accuracy and simplicity K-M...

Mutual information (MI) based registration methods are susceptible to the variation of the intensity of the image. We present a multi-modality MRI-CT non-rigid registration method by combining Kmeans clustering technique with mutual information. This method makes use of K-means clustering to determine variant bin sizes in CT image. The resulting clustered (labeled) CT image is non-rigidly regis...

Data clustering has been applied in various fields like machine learning, data processing, wireless sensor network and pattern recognition. Association rule in data mining technique frequently plays a necessary role. Data mining has types of application areas like clustering in WSN, medical speciality and biological sequences. The disadvantages and advantages of K-means and PSO technique have b...

This paper extends upon our previous work using i-vectors for speaker diarization. We examine the effectiveness of spectral clustering as an alternative to our previous approach using Kmeans clustering and adapt a previously-used heuristic to estimate the number of speakers. Additionally, we consider an iterative optimization scheme and experiment with its ability to improve both cluster assign...

Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Parti...

In this text we propose a method which efficiently performs clustering of high-dimensional data. The method builds on random projection and the Kmeans algorithm. The idea is to apply K-means several times, increasing the dimensionality of the data after each convergence of K-means. We compare the proposed algorithm on four high-dimensional datasets, image, text and two synthetic, with K-means c...

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present kANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular kmeans algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, avera...

Properties of corpora, such as the diversity of vocabulary and how tightly related texts cluster together, impact the best way to cluster short texts. We examine several such properties in a variety of corpora and track their effects on various combinations of similarity metrics and clustering algorithms. We show that semantic similarity metrics outperform traditional n-gram and dependency simi...