The sewerage system is a primary element of a city and is responsible for the congestion of both rain and gray water from homes and industries. It is essential to have a monitoring system and a plan to perform prior expansion in the sewerage management system, to avoid massive disruption. However, there is no monitoring system in several overpopulated cities in the world, and the expansion process faces myriad difficulties and takes much time. This paper presents a model for an intelligent sewerage management system that provides real-time monitoring without any major changes to the previous system, using water sensors, a Global System for Mobile Communications (GSM) module, and a micro-controller. The condition of the sewerage acts as an input through the sensors; then, the microcontroller stores the value in the cloud and performs waste collection depending on the current situation. Meanwhile, after processing, the information reaches the monitoring system. Various trial installations of the proposed system have shown that it enables real-time monitoring to observe live conditions and helps to prevent sewerage blockage caused by solid waste. Considering a deficient cost model, this system can intensify the performance of poorly managed sewerage systems.
<p>Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.</p>
<p>The amount of data has been increasing exponentially in every sector such as banking securities, healthcare, education, manufacturing, consumer-trade, transportation, and energy. Most of these data are noise, different in shapes, and outliers. In such cases, it is challenging to find the desired data clusters using conventional clustering algorithms. DBSCAN is a popular clustering algorithm which is widely used for noisy, arbitrary shape, and outlier data. However, its performance highly depends on the proper selection of cluster radius <em>(Eps)</em> and the minimum number of points <em>(MinPts)</em> that are required for forming clusters for the given dataset. In the case of real-world clustering problems, it is a difficult task to select the exact value of Eps and <em>(MinPts)</em> to perform the clustering on unknown datasets. To address these, this paper proposes a dynamic DBSCAN algorithm that calculates the suitable value for <em>(Eps)</em> and <em>(MinPts)</em> dynamically by which the clustering quality of the given problem will be increased. This paper evaluates the performance of the dynamic DBSCAN algorithm over seven challenging datasets. The experimental results confirm the effectiveness of the dynamic DBSCAN algorithm over the well-known clustering algorithms.</p>
The process which was used for grouping the similar elements or occurring closely is called cluster. Nowadays cluster analysis is one of the major data analysis techniques. On the other hand many important problems involve clustering for large datasets. KSOM and k-means is one of the most popular partitioning clustering algorithms that are widely used. The original k-means algorithm is computationally expensive and the number of clusters K, to be specified before the algorithm is applied. The other thing is, it is quite sensitive to initial centroids. When more number of dimensions is added then K-Means fails to give optimum result. For this "Curse of High Dimensionality" problem is occurred. Here we propose that Kohonen Self Organizing Map (KSOM) is used to define number of clusters and then load based initial centroid K-Means algorithm (KSOMKM) is used to find out the more accurate number of cluster for High Dimensional Dataset. Finally the Kohonen Self Organizing Map (KSOM) with Load based K-Means algorithm (KSOMKM) is tested on different datasets. There are an IRIS data set, Diabetes dataset, Thyroid, Blood pressure dataset. Its performance is compared with other clustering algorithm for number of iteration, quantization errors and topographic errors. Index Terms-curse of dimensionality, data mining, high-dimensional datasets and Kohonen Self Organizing Map (KSOM).
Data analytics (DA) is the process of exploring datasets in order to illustrate conclusions about the information they contain. It is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information. A database contains both structured and semi-structured data. The semi-structured data are from different sources. The system is provided with an open grid rule generation for analyzing the data from whole data container. According to this concept, the analysis is much absolute rather than other, data mining technique. The main objective of the proposed study is to provide data having better and significant perspective.
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