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Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
As a major part of household electricity load in summer, air conditioning (AC) consumption demonstrates enormous demand response (DR) potentials and numerous AC based DR programs are carried out annually. Understanding electricity consumption behaviors is one of the key elements for utilities to design more cost-effective DR programs. So studying on the AC electricity usage habits is of great significance...
Good nutrition is an essential component of life. Undernutrition is the root cause of death of over 3.5 million children under the age of five in India. To address this issue of malnutrition, though overarching national policy is desirable, it may not be effective if the root cause of malnutrition varies across regions of the country. In this context, the attempt made in this paper is two-fold. First,...
Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation...
The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation...
In this paper, a hybrid method for acquisition and reconstruction of sparse magnetic resonance images is presented. The method uses conventional spin echo Magnetic Resonance Imaging (MRI) with only a few Phase-encoding steps to obtain the dominant k-space data coefficients. The rest of the k-space data coefficients are estimated using Compressive Sampling (CS). The compressive sampling part of the...
Existing clustering algorithms need to specify the number of clusters and to select initial points using human input, which lead to inferior clustering and optimisation outputs. Here, an improved grey decision-making model based on the thought of affinity propagation algorithm and grey correlation analysis is proposed to solve these problems. According to the panel data class and the inter-class candidate...
Data clustering methods have been used extensively for image segmentation in the past decade. In our previous work, we had established that combining the traditional clustering algorithms with a meta-heuristic like Firefly Algorithm improves the stability of the output as well as the speed of convergence. In this paper, we have replaced the Euclidean distance formula with kernels. We have combined...
In this paper, we propose a persistent scatterer clustering method for high-resolution structure displacement analysis. Persistent scatterer interferometry can monitor millimetric displacement of structures like bridges, buildings, and roads by analysis at persistent scatterers (PSs), pixels with high coherence in synthetic aperture radar (SAR) images. However, it requires great time and effort to...
This paper introduces an approach to outlier mining in the context of rule-based knowledge bases. Rules in knowledge bases are a very specific type of data representation and it is necessary to analyze them carefully, especially when they differ from each other. The goal of the paper is to analyze the influence of using different similarity measures and clustering methods on the number of outliers...
Learning Management System, such as Moodle, has been utilized extensively as part of e-learning implementation for higher institutions. The flexibility of LMS to convey the learning materials in many ways and approaches enable the instructor to implement blended learning. The student's interaction and activities while learning are captured by Moodle in the log data file and are useful to identify...
Clustering is a common data mining procedure that groups multi-dimensional points with similar components to form different subsets. Among all of the clustering algorithms, DBSCAN is one of the most popular algorithms owing to finding clusters with arbitrary shapes and noise of datasets. However, with data volumes growing and the execution time of algorithms becoming longer, numerous methods have...
Image segmentation is the process of separating objects within an image. Image segmentation can be considered as an important computer vision problem in image sensing where the homogeneous regions in an image can be distinguished with high accuracy. In this study, a two stage hybrid method has been proposed for image segmentation. In the first stage, the Histogram Based Cluster Estimation (HBCE) is...
The efficiency of a WiFi system with dozens of base stations in relatively small physical area is determined by the optimal allocation of the radio channels to the mobile devices. Based on the increased penetration rate of the high traffic capable smartphones and accentuated usage of these devices in densely populated buildings intelligent hardware tools are needed to offer QoS level to the users...
Clustering is the unsupervised process of assigning entities into groups based on similarities among those entities. Image clustering is the crucial step of mining satellite images. As the satellite imagery is getting generated at a higher rate than the previous decades, it becomes essential to have better solutions in terms of accuracy as well as performance. In this paper, we are proposing the solution...
This paper focuses on the content of test cases, and categorizes test cases into clusters using the similarity between test cases, their degree of similarity is obtained through a morphological analysis. If there are two similar test cases, they would test the same or similar functionalities in similar but different conditions. Thus, when one of them is run for a regression testing, the remaining...
MicroRNAs form a family of single strand RNA molecules having length of approximately 22 nucleotides that are present in all animals and plants. Various studies have revealed that microRNA tend to cluster on chromosomes. In this regard, a novel clustering algorithm is presented in this paper, integrating rough hypercuboid approach with fuzzy c-means. Using the concept of rough hypercuboid equivalence...
Bayesian nonparametric (BNP) models have recently become popular due to their flexibility in identifying the unknown number of clusters. However, they have difficulties handling heterogeneous data from multiple sources. Existing BNP methods either treat each of these sources independently - hence do not get benefits from the correlating information between them, or require to explicitly specify data...
Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources. In this paper, we...
This paper presents insights into the proportions between the k-means clusters of successful Differential Evolution (DE), donor generating vectors. This is demonstrated by the high certainty that these proportions are similar - and thereby, that these cluster size proportions regularly appear. A characteristic of these proportions is that they are observed at the same specific values in different...
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