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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...
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...
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...
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...
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...
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...
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...
The clustering is the most effective method to identify the outliers in the UCI Repository dataset. This paper proposes detecting outliers on UCI datasets using Adaptive Rough Fuzzy C-Means clustering algorithm. In the first phase of the Adaptive Rough Fuzzy C- Means algorithm, the Rough k means algorithm is used for pre-processing of UCI repository dataset and it is normally identify the outliers...
Model-free reinforcement learning algorithms based on POMDP has been devised and adopted for many years. The complexity of the environment where the agent works determines the necessity of dealing with the whole observation space. Therefore, instance-based learning methods have been put forward. NSM, USM and U-Tree algorithms can present the whole observation space as instance chains, which are very...
In software projects, there is a data repository which contains the bug reports. These bugs are required to carefully analyse and resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the deleying in addressing some important bugs resolutions. To overcome this problem, researchers have introduced many techniques. One of the commonly used algorithm...
The Indian Stock market, with over 5000 listed companies, where sorting of companies requires analysis of numerous financial parameters and ratios, is an example of a database from which single sub set or group of companies need to be found out for investing. With growing popularity of artificial intelligence in stock trading, it has been applied heavily into stock market for picking stocks. We apply...
Clustering is one of the data mining techniques used in a knowledge discovery process. It is assumed that a good representation of data points may yield good clustering results [6]. This paper discusses the effect of the coordinate system on the clustering. In this paper, we propose a density based clustering approach to group objects represented using Polar coordinate system. The experiment is carried...
Fuzzy comprehensive evaluation algorithm according to individuals of the same class of things has a greater similarity, based on the principles of individual differences in different classes, make the greater similarity degree of data or indicators with classify, establish clear category boundaries, according to similarity of indicators, to cluster factor, to form relationship clusters of arbitrary...
Clustering is one of the most widely studied problem in machine learning and data mining. The algorithms for clustering depend on the application scenario and data domain. K-Means algorithm is one of the most popular clustering techniques that depend on distance measure. In this work, an extensive empirical evaluation of three significant variations of K-Means algorithm is carried out on the basis...
Data mining advances as a promising solution in exploring knowledge concealed in database and clustering is one its application. Clustering can be explained as the unconfirmed categorization of patterns into groups. It is the task of combining a set of objects into diverse subsets such that objects belonging to the similar cluster are extremely related to each other. Various objective functions are...
The preeminent intention of the proposed study is exploring the performance of the Brainstorm Optimization algorithm in Hard c-means clustering of data. The rationale behind this analysis is to generate a random solution set of centroids and then modify the centroids so as to refine the clusters. As we are using Brainstorm Optimization which is a form of evolutionary algorithm this refinement of centroid...
k-means algorithm, in spite of its computational efficiency and capacity for faster convergence has some serious drawbacks like its tendency to stick into local optima and the requirement of supplying number of cluster before execution. Our algorithm used Differential Evolution (DE) as preprocessor to overcome those bottlenecks. Experiments show that the improved version of clustering algorithm produces...
Application of clustering algorithms for investigating real life data has concerned many researchers and vague approaches or their hybridization with other analogous approaches has gained special attention due to their great effectiveness. Recently, rough intuitionistic fuzzy c-means algorithm has been proposed by Tripathy et al [3] and they established its supremacy over all other algorithms contained...
In big data analytics, clustering plays a fundamental and decisive role in supporting pattern mining and value creation. To help improve user experience and satisfaction level of clustering algorithms, one important key is to let users define the quality of the aggregated clusters (e.g. In terms of the homogeneity and the relative population of each resulting cluster) they prefer instead of to fix...
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