Project Proposal ( Option 3 )

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. Cambridge (Mass.): MIT Press.

Also, it is possible that the k-means algorithm won't find a final solution.  In this case it would be a good idea to consider stopping the algorithm after a pre-chosen maximum of

This file contains the exercises, hints, and solutions for Chapter 1 of the book ”Introduction to the Design and Analysis of Algorithms,” 2nd edition, by A. Levitin. The problems that might be challenging for at least some students are marked by ; those that might be difficult for a majority of students are marked by .

Methods and apparatuses are provided that employ an improved greedy algorithm for addressing NP-Hard problems and others like them. The improved greedy algorithm considers possible local savings while also remaining significantly fast.

Key equation solver, which determines the error locator polynomial through the BM (Berlekamp-Massey) algorithm as follows:

Hazarika M., Laha D., Machine-part cell formation for maximum grouping efficacy based on genetic algorithm, 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), 2015, pp 1-6, DOI: 10.1109/WCI.2015.7495521

One main point presented in the article is That the algorithm presented by the author is not only more

Daftardar-Gejji and Jafari [17] have introduced a new iterative method (NIM) which is simple to

“By taking the reference of the example problem (page-223) in textbook (Algorithm Design - Jon Kleinberg and Eva Tardos ) we solve this problem.”

The clustering hierarchy for text clustering or documents clustering we used cluster dendrogram presentation by R implementation. Some people find cluster dendrograms to be fairly clear to read. Others simply find them perplexing. Here, we can see many more groups that are identifiable in the dendrogram. Here, I have arbitrarily chosen to look at five clusters, as indicated by the red boxes in figure

Online Heuristic with CST. To solve the above problem, we initially make the seed clusters with the existing nodes/users using CST and once we have formed 'K' equal size coherent clusters, we switch to Online Heuristic for real time performance.

Results for 81×81grid with Re=1000 are shown in Figure 1.It contains contours and graphs at Re =1000, with convergence criteria =1e-6, No of iterations = 15000, and No of time steps =50000.

BS has its source in the fact that hyperspectral bands have high correlation. For a highly correlated subset of bands, we may need only the most representative one. As a result, BS is associated with data clustering. In this paper, each band of hyperspectral data are regarded as a data point in high-dimensional space. The main purpose of this paper is to present an algorithm that can determine the exemplars of these points accurately and efficiently. This method utilizes two reasonable assumptions: First the cluster centers must have the highest local density (i) , and the second is that they have comparatively larger distance (i) to the points of gretaer density. In the following, we first demonstrate the computation of ρi and δi in detail.

I will conduct a comparative analysis of multiple force-directed algorithms used to identify clusters in biological networks. The analysis will consider topics such as the algorithm process, amount of preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K-Means, SPICi, Markov Clustering, RNSC, and PBD will be used for the comparison. I will identify the best algorithm according to my analysis for each type of input data studied.

In this paper, to clarify the anomaly, we improve the precision of inversion result, in minimum inertia; in Ardestani and Nejati (2011) algorithm by