Clustering Algorithms

In this paper, node localization algorithms in wireless sensor networks are researched, the traditional algorithms are studied, and some meaningful results are obtained. For the localization algorithm and route planning of WSN exists a big localization error in wireless communication. WSN communication system is researched. According to the anchor nodes and unknown nodes, a new localization algorithm and route planning method of WSN are proposed in this paper. At the same time, a new genetic algorithm of route planning of WSN is proposed. The performance of the node density and localization error is simulated and analyzed. The simulation results show that the performance of proposed WSN localization algorithm and route planning method are better than the traditional algorithms.

Mercu Buana University Campus D is part of Mercu Buana University which began the operational in 2013. Since 2013 until 2017, Mercu Buana University Campus D still got less than a target about getting the new student.This can be due to various things including the lack of precisely marketing strategy undertaken.Therefore, in this study the authors make an application by implementing the concept of data mining using clustering and forecasting methods to obtain information from existing data registrants. So, the information can be used by decision makers to determine effective and efficient marketing strategies.

The development of systems capable of recognizing and categorising emotions is of interest to researchers in various scientific areas including artificial intelligence. The traditional notion that emotions and rationality are two separate realms has gradually been challenged. The work of neurologists has shown the strong relationship between emotional episodes and the way humans think and act. Furthermore, emotions not only regulate human decisions but could also contribute to a more satisfactory response to the environment, i.e., faster and more precise actions. In this paper an analysis of physiological signals employed in real-time emotion detection is presented in the context of Intelligent Inhabited Environments (IIE). Two studies were performed to investigate whether physical exertion has a significant effect on bodily signals stemming from emotional episodes with subjects having various degrees of affect intensity: 1) a statistical analysis using the Wilcoxon Test, and 2) a cluster analysis using the Davies- Bouldin Index. Preliminary results demonstrated that the heart rate and skin resistance consistently showed similar changes regardless of the physical stimuli while blood volume pressure did not show a significant change. It was also found that neither physical stress nor affect intensity played a role in the separation of neutral and non-neutral emotional states.

An ant colony optimization approach for partitioning a set of objects is proposed. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put in a class with a probability that depends on the distance between the object and the centroid of the class (visibility) and the pheromone trail; the latter depends on the class memberships that have been defined along the iterations. The procedure is improved with the application of K-means algorithm in some iterations of the ant colony method. We performed a simulation study in order to evaluate the method with a Monte Carlo experiment that controls some sensitive parameters of the clustering problem. After some tuning of the parameters, the method has also been applied to some benchmark real-data sets. Encouraging results were obtained in nearly all cases.

Leukemia is a fatal disease of the type "Blood Cancer", where the White Blood Cells (WBC) increases in human bone marrow and peripheral blood. Acute Lymphoblastic Leukemia (ALL) is a common types of leukemia that affects young children of below 10 years and adults over 60 years, aroused by accumulation and overproduction of immature and cancerous cells identified as lymphoblasts. At present, the diagnosis of ALL includes measures alike performing a full blood count, bone marrow biopsy, blood picture, immunophenotyping, cytochemical stain and cytogenetics. These medicinal techniques are highly tedious, costly, requires expertise of hematologists and available only in few hospitals especially in developing countries. Hence, as an alternative, use of image processing and machine learning to diagnose ALL would become an effective solution. Even though, several research groups have used image processing to detect and diagnose ALL, recognition and splitting of overlapping Red Blood Cells (RBC) with WBC has however been a challenging issue. This paper is about a research study and an application that includes an image processing and machine learning algorithm to diagnose ALL while attempting to solve the issue of overlapping cells. The research is also extended to detect the quality devastation in blood films in terms of storing them for prolonged period. The inputs for this application include microscopic peripheral blood films of ALL patients and healthy individuals obtained Lanka. This research project has received verification of ethical approval from Faculty of Medicine, General Sir John Kotelawala Defence University, Sri Lanka. In the developed application, segmentation using morphological operations in OpenCV Python and supervised learning based classification using K-Nearest Neighbour implementation has been proposed in detection and diagnosing of ALL. As per the results, the proposed algorithm has led to a high accuracy of 88.8% in diagnosing ALL. The end product includes a Python based QT GUI based development suite that performs main targeted backend functionalities and a PHP based web application that serves hematologists, doctors and patients to perform utility functions.

Electrocardiogram can be occasionally or continuously measured from living Human beings. Regardless of their Disease, Now a day’s physicians or doctors are suggesting to take ECG. These signals reflect the physiological processes and electrical activity of the Heart. Therefore, the study of ECG signals is essential for both medical applications and scientific studies for this purpose one requires best clustering method. It is difficult to provide a best clustering methods for the ECG signals because these categories may overlap, so that a method may have features from several categories. Nevertheless, it is useful to present a relatively organized picture of the different clustering methods.

Detecting overlapping structures and identifying non-linearly-separable clusters with complex shapes are two major issues in clustering. This paper presents two kernel based methods that produce overlapping clusters with both linear and nonlinear boundaries. To improve separability of input patterns, we used for both methods Mercer kernel technique. First, we propose Kernel Overlapping K-means I (KOKMI), a centroid based method, generalizing kernel k-means to produce non-disjoint clusters with nonlinear separations. Second, we propose Kernel Overlapping K-means II (KOKMII), a medoid based method improving the previous method in terms of efficiency and complexity. Experiments performed on non-linearly-separable and real multi-labeled data sets show that proposed learning methods outperform the existing ones.

Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Weka is a type of data mining tools. It is contain the many machine leaning algorithms. It provides the facility to classify our data through various algorithms. In this paper we are studying the various clustering algorithms. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar to each other than to those in other clusters. The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Our main aim to show the comparison of the different samples of data like we are using different Emails with similar text through different clustering algorithms of Weka and find out which parameter of Weka tool is effective for the users for data mining or email mining.

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