Tree structure for efficient data mining using rough sets

In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.

Rough Core Vector Clustering

Support Vector Clustering has gained reasonable attention from the researchers in exploratory data analysis due to firm theoretical foundation in statistical learning theory. Hard Partitioning of the data set achieved by support vector clustering may not be acceptable in real world scenarios. Rough Support Vector Clustering is an extension of Support Vector Clustering to attain a soft partitioning of the data set. But the Quadratic Programming Problem involved in Rough Support Vector Clustering makes it computationally expensive to handle large datasets. In this paper, we propose Rough Core Vector Clustering algorithm which is a computationally efficient realization of Rough Support Vector Clustering. Here Rough Support Vector Clustering problem is formulated using an approximate Minimum Enclosing Ball problem and is solved using an approximate Minimum Enclosing Ball finding algorithm. Experiments done with several Large Multi class datasets such as Forest cover type, and other Multi class datasets taken from LIBSVM page shows that the proposed strategy is efficient, finds meaningful soft cluster abstractions which provide a superior generalization performance than the SVM classifier.

Cytochrome c oxidase is preferentially synthesized in the rough endoplasmic reticulum--mitochondrion complex in rat liver

The specific activity and content of cytochrome oxidase in the rough endoplasmic reticulum--mitochondrion complex are higher than in the mitochondrial fraction. Radiolabelling studies with the use of hepatocytes and isolated microsomal and rough endoplasmic reticulum--mitochondrion fractions, followed by immunoprecipitation with anti-(cytochrome oxidase) antibody, reveal that the nuclear-coded cytoplasmic subunits of cytochrome oxidase are preferentially synthesized in the latter fraction. The results have a bearing on the mechanism of transport of these subunits into mitochondria.

Modeling growth of hydrogen bubbles in aluminum castings using the level-set method

A new approach is proposed to solve for the growth as well as the movement of hydrogen bubbles during solidification in aluminum castings. A level-set methodology has been adopted to handle this multiphase phenomenon. A microscale domain is considered and the growth and movement of hydrogen bubbles in this domain has been studied. The growth characteristics of hydrogen bubbles have been evaluated under free growth conditions in a melt having a hydrogen input caused b solidification occurring around the microdomain.

Set theoretic operations on polygons using the scan-grid approach

This paper describes an algorithm to compute the union, intersection and difference of two polygons using a scan-grid approach. Basically, in this method, the screen is divided into cells and the algorithm is applied to each cell in turn. The output from all the cells is integrated to yield a representation of the output polygon. In most cells, no computation is required and thus the algorithm is a fast one. The algorithm has been implemented for polygons but can be extended to polyhedra as well. The algorithm is shown to take O(N) time in the average case where N is the total number of edges of the two input polygons.

Bi-criteria single machine scheduling problem with a learning effect: Aneja-Nair method to obtain the set of optimal sequences

In this paper, we consider the bi-criteria single machine scheduling problem of n jobs with a learning effect. The two objectives considered are the total completion time (TC) and total absolute differences in completion times (TADC). The objective is to find a sequence that performs well with respect to both the objectives: the total completion time and the total absolute differences in completion times. In an earlier study, a method of solving bi-criteria transportation problem is presented. In this paper, we use the methodology of solvin bi-criteria transportation problem, to our bi-criteria single machine scheduling problem with a learning effect, and obtain the set of optimal sequences,. Numerical examples are presented for illustrating the applicability and ease of understanding.

Structural origin of set-reset processes in Ge15Te83Si2 glass investigated using in situ Raman scattering and transmission electron microscopy

We report here that the structural origin of an easily reversible Ge15Te83Si2 glass can be a promising candidate for phase change random access memories. In situ Raman scattering studies on Ge15Te83Si2 sample, undertaken during the amorphous set and reset processes, indicate that the degree of disorder in the glass is reduced from off to set state. It is also found that the local structure of the sample under reset condition is similar to that in the amorphous off state. Electron microscopic studies on switched samples indicate the formation of nanometric sized particles of c-SiTe2 structure. ©2009 American Institute of Physics

Impact of Energy Quantization on the Performance of Current-Biased SET Circuits

The current-biased single electron transistor (SET) (CBS) is an integral part of almost all hybrid CMOS SET circuits. In this paper, for the first time, the effects of energy quantization on the performance of CBS-based circuits are studied through analytical modeling and Monte Carlo simulations. It is demonstrated that energy quantization has no impact on the gain of the CBS characteristics, although it changes the output voltage levels and oscillation periodicity. The effects of energy quantization are further studied for two circuits: negative differential resistance (NDR) and neuron cell, which use the CBS. A new model for the conductance of NDR characteristics is also formulated that includes the energy quantization term.

Approximation algorithm for maximum independent set in planar traingle-free graphs

The maximum independent set problem is NP-complete even when restricted to planar graphs, cubic planar graphs or triangle free graphs. The problem of finding an absolute approximation still remains NP-complete. Various polynomial time approximation algorithms, that guarantee a fixed worst case ratio between the independent set size obtained to the maximum independent set size, in planar graphs have been proposed. We present in this paper a simple and efficient, O(|V|) algorithm that guarantees a ratio 1/2, for planar triangle free graphs. The algorithm differs completely from other approaches, in that, it collects groups of independent vertices at a time. Certain bounds we obtain in this paper relate to some interesting questions in the theory of extremal graphs.

The real area of contact and compliance of rough cylinders in compression

Bearing area analysis has been used to study the real area of contact and compliance of rough turned steel cylinders in compression. Calculations show that the elastic real area of contact is very small compared to the plastic real area of contact, and that local compliance due to flattening of asperity tips is a small proportion of the total compliance obtained from experiments. The fact that increased load brings more and more new asperities under load rather than enlarging the contact spots leads to a rather simple load-compliance relation for a rough cylinder, viz., W' = Nh · K1δn, where W0 = K1δn defines the load-compliance relation of the individual asperities, and Nh represents the number of asperities bearing the load.

real area of contact and compliance of rough cylinders in compression

Bearing area analysis has been used to study the real area of contact and compliance of rough turned steel cylinders in compression. Calculations show that the elastic real area of contact is very small compared to the plastic real area of contact, and that local compliance due to flattening of asperity tips is a small proportion of the total compliance obtained from experiments. The fact that increased load brings more and more new asperities under load rather than enlarging the contact spots leads to a rather simple load-compliance relation for a rough cylinder, viz., W' = Nh · K1δn, where W0 = K1δn defines the load-compliance relation of the individual asperities, and Nh represents the number of asperities bearing the load.

Analytical method for solving a set of inhomogeneous infinite simultaneous equations

It is shown that a method based on the principle of analytic continuation can be used to solve a set of inhomogeneous infinite simultaneous equations encountered in the analysis of surface acoustic wave propagation along the periodically perturbed surface of a piezoelectric medium.

Analytical method for solving a set of infinite simultaneous equations

It is shown that a method based on the principle of analytic continuation can be used to solve a set of infinite simultaneous equations encountered in solving for the electric field of a periodic electrode structure.

Assessment of SET Logic Robustness Through Noise Margin Modeling

A compact model for noise margin (NM) of single-electron transistor (SET) logic is developed, which is a function of device capacitances and background charge (zeta). Noise margin is, then, used as a metric to evaluate the robustness of SET logic against background charge, temperature, and variation of SET gate and tunnel junction capacitances (CG and CT). It is shown that choosing alpha=CT/CG=1/3 maximizes the NM. An estimate of the maximum tolerable zeta is shown to be equal to plusmn0.03 e. Finally, the effect of mismatch in device parameters on the NM is studied through exhaustive simulations, which indicates that a isin [0.3, 0.4] provides maximum robustness. It is also observed that mismatch can have a significant impact on static power dissipation.

Electrical switching and in situ Raman scattering studies on the set-reset processes in Ge-Te-Si glass

Bulk Ge15Te83Si2 glass has been found to exhibit memory-type switching for 1 mA current with a threshold electric field of 7.3 kV/cm. The electrical set and reset processes have been achieved with triangular and rectangular pulses, respectively, of 1 mA amplitude. In situ Raman scattering studies indicate that the degree of disorder in Ge15Te83Si2 glass is reduced from off to set state. The local structure of the sample under reset condition is similar to that in the off state. The Raman results are consistent with the switching results which indicate that the Ge15Te83Si2 glass can be set and reset easily. (C) 2007 American Institute of Physics.