A Novel Analysis of Clustering for Minimum Spanning Tree using Divide

Because of their capability to distinguish groups with sporadic limits, least spanning treebased grouping calculations have been generally utilized within practice. Be that as it may, in such bunching calculations, the quest for closest neighbour in the development of least spanning trees is the primary wellspring of processing and the standard results take O(N 2) time. In this paper, we exhibit a quick least spanning tree-motivated grouping calculation, which, by utilizing a proficient execution of the cut and the cycle property of the least spanning trees, can have much preferable execution than O(N 2)

Memory and I/O optimized rectilinear steiner minimum tree routing for VLSI

As the size of devices are scaling down at rapid pace, the interconnect delay play a major part in performance of IC chips. Therefore minimizing delay and wire length is the most desired objective. FLUTE (Fast Look-Up table) presented a fast and accurate RSMT (Rectilinear Steiner Minimum Tree) construction for both smaller and higher degree net. FLUTE presented an optimization technique that reduces time complexity for RSMT construction for both smaller and larger degree nets. However for larger degree net this technique induces memory overhead, as it does not consider the memory requirement in constructing RSMT. Since availability of memory is very less and is expensive, it is desired to utilize memory more efficiently which in turn results in reducing I/O time (i.e. reduce the number of I/O disk access). The proposed work presents a Memory Optimized RSMT (MORSMT) construction in order to address the memory overhead for larger degree net. The depth-first search and divide and conquer approach is adopted to build a Memory optimized tree. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in terms of computation time, memory overhead and wire length. The experimental results show that the proposed model is scalable and efficient

Fully dynamic maintenance of euclidean minimum spanning trees

We maintain the minimum spanning tree of a point set in the plane, subject to point insertions and deletions, in time O(n^5/6 log1^2/2 n) per update operation. No nontrivial dynamic geometric minimum spanning tree algorithm was previously known. We reduce the problem to maintaining bichromatic closest pairs, which we also solve in the same time bounds. Our algorithm uses a novel construction, the ordered nearest neighbors of a sequence of points. Any point set or bichromatic point set can be ordered so that this graph is a simple path

Performanace of Improved Minimum Spanning Tree Based on Clustering Technique

Clustering technique is one of the most important and basic tool for data mining. Cluster algorithms have the ability to detect clusters with irregular boundaries, minimum spanning tree-based clustering algorithms have been widely used in practice. In such clustering algorithms, the search for nearest objects in the construction of minimum spanning trees is the main source of computation and the standard solutions take O(N2) time. In this paper, we present a fast minimum spanning tree-inspired clustering algorithm, which, by using an efficient implementation of the cut and the cycle property of the minimum spanning trees, can have much better performance than O(N2)