K-Modes is an eminent algorithm for clustering data set with categorical attributes. This algorithm is famous for its simplicity and speed. The KModes is an extension of the K-Means algorithm for categorical data. Since K-Modes is used for categorical data so ‘Simple Matching Dissimilarity’ measure is used instead of Euclidean distance and the ‘Modes’ of clusters are used instead of ‘Means’. Ho...

New unstable temperature gradient driven modes in an inhomogeneous tokamak plasma are identified. These modes represent temperature gradient (ion and electron) driven modes destabilized in the short wavelength regions with k( perpendicular )rho(i,e)>>1, respectively. The instability occurs due to a specific plasma response that significantly deviates from Boltzmann distribution in the regions k...

The K-modes clustering has received much attention, since it works well for categorical data sets. However, the performance of K-modes clustering is especially sensitive to the selection of initial cluster centers. Therefore, choosing the proper initial cluster centers is a key step for K-modes clustering. In this paper, we consider the initialization of K-modes clustering from the view of outl...

We present a rigid-ion model of Ba, zK,BiO, for compositions x = 0.25 and x = 0.40. Partial vibrational density ot states of ions in the compounds Ba,, ,? K,, ,,BiO, and Ba,, (,Ki, ,BiO, are reported at room temperature and 100 K. The oxygen modes dominate the vibrational spectrum from 150 up to 82Ocm ‘. The low frequency modes arc associated with the cations. The number of substituted K around...

The K-Modes clustering algorithm [1] has shown great promise for clustering large data sets with categorical attributes. K-Mode clustering algorithm suffers from the drawback of choosing random selection of initial points (modes) of the cluster. Different initial points leads to different cluster formations. In this paper Density-based Multiscale Data Condensation [2] approach with hamming dist...

We present neutron scattering measurements of the dynamic structure factor of liquid helium confined to nanoscales in 25 and 34 Å mean pore diameter porous media over a range of pressures and temperatures. At low temperature and low pressure we observe well-defined phonon-roton P-R modes characteristic of liquid helium containing Bose-Einstein condensation BEC . As pressure is increased above 2...

Suppose that A ∈ RN×N is symmetric positive semidefinite with rank K ≤ N . Our goal is to decompose A into K rank-one matrices ∑K k=1 gkg T k where the modes {gk} K k=1 are required to be as sparse as possible. In contrast to eigen decomposition, these sparse modes are not required to be orthogonal. Such a problem arises in random field parametrization where A is the covariance function and is ...

With the advent of the k-modes algorithm, the toolbox for clustering categorical data has an efficient tool that scales linearly in the number of data items. However, random initialization of cluster centers in k-modes makes it hard to reach a good clustering without resorting to many trials. Recently proposed methods for better initialization are deterministic and reduce the clustering cost co...

Using the next-to-leading order low energy effective Hamiltonian for ∆B = 1 transitions, the effects of electroweak penguin operators in some two-body decay modes of B s meson are estimated in the Standard Model (SM). We find that in B s → π+K− and B s → K+K− decay modes, the electroweak penguin effects are small, while in B s → πK̄0, φK̄0, φφ, the electroweak penguin operators enhance or reduce ...

The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based init...