Improved Dynamic Parallel K-Means Algorithm using Dunn?s Index Method

K-Means is popular and widely used clustering technique in present scenario. Many research has been done in same area for the improvement of K-Means clustering algorithm, but further investigation is always required to reveal the answers of the important questions such as ?is it possible to find optimal number of clusters dynamically while ignoring the empty clusters? or ?does the parallel execution of any clustering algorithm really improves it performance in terms of speedup?. This research presents an improved K-Means algorithm which is capable to calculate the number of clusters dynamically using Dunn?s index approach and further executes the algorithm in parallel using the capabilities of Microsoft?s Task Parallel Libraries. The original K-Means and Improved parallel modified K-Means algorithm performed for the two dimensional raw data consisting different numbers of records. From the results it is clear that the Improved K-Means is better in all the scenarios either increase the numbers of clusters or change the number of records in raw data. For the same number of input clusters and different data sets in original K-Means and Improved K-Means, the performance of Modified parallel K-Means is 20 to 50 percent better than the original K-Means in terms of Execution time and Speedup

A New Approach of Detecting Network Anomalies using Improved ID3 with Horizontal Partioning Based Decision Tree

In this paper we are proposing a new approach of Detecting Network Anomalies using improved ID3 with horizontal portioning based decision tree. Here we first apply different clustering algorithms and after that we apply horizontal partioning decision tree and then check the network anomalies from the decision tree. Here in this paper we find the comparative analysis of different clustering algorithms and existing id3 based decision tree

A Modified Version of the K-means Clustering Algorithm

Clustering is a technique in data mining which divides given data set into small clusters based on their similarity. K-means clustering algorithm is a popular, unsupervised and iterative clustering algorithm which divides given dataset into k clusters. But there are some drawbacks of traditional k-means clustering algorithm such as it takes more time to run as it has to calculate distance between each data object and all centroids in each iteration. Accuracy of final clustering result is mainly depends on correctness of the initial centroids, which are selected randomly. This paper proposes a methodology which finds better initial centroids further this method is combined with existing improved method for assigning data objects to clusters which requires two simple data structures to store information about each iteration, which is to be used in the next iteration. Proposed algorithm is compared in terms of time and accuracy with traditional k-means clustering algorithm as well as with a popular improved k-means clustering algorithm

STUDI KOMPARATIF PENERAPAN METODE HIERARCHICAL, K-MEANS DAN SELF ORGANIZING MAPS (SOM) CLUSTERING PADA BASIS DATA

This study identifies the results of some test results clustering methods. The data set used in this test method Clustering. The third method of clustering based on these factors than the size of the data set and the extent of the cluster. The test results showed that the SOM algorithm produces better accuracy in classifying objects into matching groups. K-means algorithm is very good when using large data sets and compared with Hierarchical SOM algorithm. Hierarchical grouping and SOM showed good results when using small data sets compared to using k-means algorithm

APPLICATION OF CLUSTER ANALYSIS IN THE BEHAVIOUR OF TRAFFIC PARTICIPANTS RELATING TO THE USE OF SAFETY SYSTEMS AND MOBILE PHONES

This paper presents a cluster analysis related to the behavior of traffic participants in relation to the use of safety systems and mobile phones. The data on traffic behavior were downloaded from an open data portal in Serbia. Three types of cluster analysis have been applied: hierarchical clustering, Bayesian Information Criterion (BIC) clustering and model clustering. The obtained results point to the various possibilities of using these three clustering methods in the field of traffic and suggest further research

Fuzzy and non-fuzzy approaches for digital image classification

This paper classifies different digital images using two types of clustering algorithms. The first type is the fuzzy clustering methods, while the second type considers the non-fuzzy methods. For the performance comparisons, we apply four clustering algorithms with two from the fuzzy type and the other two from the non-fuzzy (partitonal) clustering type. The automatic partitional clustering algorithm and the partitional k-means algorithm are chosen as the two examples of the non-fuzzy clustering techniques, while the automatic fuzzy algorithm and the fuzzy C-means clustering algorithm are taken as the examples of the fuzzy clustering techniques. The evaluation among the four algorithms are done by implementing these algorithms to three different types of image databases, based on the comparison criteria of: dataset size, cluster number, execution time and classification accuracy and k-cross validation. The experimental results demonstrate that the non-fuzzy algorithms have higher accuracies in compared to the fuzzy algorithms, especially when dealing with large data sizes and different types of images. Three types of image databases of human face images, handwritten digits and natural scenes are used for the performance evaluation

An Evaluation of Perceptual Classification led by Cognitive Models in Traffic Scenes

The objects extraction and recognition constitute the most important link in the image processing and understanding, and it cannot be achieved without a solid objects organization during the processing through the learning mechanisms. Most often, both the response time and the accuracy are undeniable criteria for applications in this field. Actually, a vision system need to take into consideration these criteria, either in the structural, the methodological or in the algorithmic aspect. Thus, we consider that the ontological study at the domain and task levels, in the vision systems, has become essential in order to provide a substantial assistance to the multitudes of applications in image processing. Concerning the domain knowledge, several patterns for structuring were proposed to improve the objects representation and organization, they often advocate the precision aspect on time and on effort devoted to the recognition. In practical terms, clustering methods only focus on the accuracy aspect within a category, without considering the recognition aspect [1]. Thus, we propose in this study a new procedure of object categorization, which uses, according to the expertise in the domain, a fit evaluation that is able to adjust the level of partitioning. As a result, this procedure will find a compromise between the accuracy on the categories and the reduction of the supplied effort in recognition.  

Quality management system and practices

Quality is a perceptual, conditional and somewhat subjective attribute of a product or service. Its meaning in business has developed over time. It has been understood differently and interpreted differently by different people. A business will benefit most through focusing on the key processes that provide their customers with products and services. Producers may measure the conformance quality, or degree to which the product or service was made according to the required specification. Customers on the other hand, may focus on the quality specification of a product or service, or compared it with those that are available in the marketplace. In a modern global marketplace, quality is a key competency which companies derive competitive advantage. Achieving quality is fundamental to competition in business in propelling business into new heights. Many quality management philosophies, methodologies, concepts and practices were created by quality gurus to manage quality of product and service in an organization. These practices have evolved over time to create sustainable sources of competitive advantage. New challenges faced by managers are addressed to improve organization’s performance and future competition. In the total quality management form, it is a structured management system adopted at every management levels that focused on ongoing effort to provide product or service. Its integration with the business plan of the organization can exact positive influence on customer satisfaction and organizational performance. This chapter dealt with what is quality and TQM, cost of quality, linking quality management system to organizational performance, its impact on organizations and approaches of implementing TQM and the quality journey

Some Clustering Methods, Algorithms and their Applications

Clustering is a type of unsupervised learning [15]. When no target values are known, or "supervisors," in an unsupervised learning task, the purpose is to produce training data from the inputs themselves. Data mining and machine learning would be useless without clustering. If you utilize it to categorize your datasets according to their similarities, you'll be able to predict user behavior more accurately. The purpose of this research is to compare and contrast three widely-used data-clustering methods. Clustering techniques include partitioning, hierarchy, density, grid, and fuzzy clustering. Machine learning, data mining, pattern recognition, image analysis, and bioinformatics are just a few of the many fields where clustering is utilized as an analytical technique. In addition to defining the various algorithms, specialized forms of cluster analysis, linking methods, and please offer a review of the clustering techniques used in the big data setting