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This paper presents the analysis of South Sumatera hotspots distribution pattern from 2005–2015 based on spatial and temporal aspects. The hotspot distribution data used is FIRMS MODIS Fires data. The spatial aspect is represented by the attribute of latitude and longitude as the location of the occurrence of hotspots and the temporal is represented by the date attribute. DBSCAN clustering is used...
In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique. This distributed approach consists of two phases: 1) local clustering phase, where each node performs a clustering on its local data, 2) aggregation phase, where the local clusters are aggregated to produce global clusters. This approach is characterised...
The study of the dynamic behaviour of the solar radiation is a very important task for PV system efficiency. Hence, we propose in this paper, a time series data mining method to detect the underlying dynamic presents in hourly solar radiation time series. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster the solar radiation time series and detect noisy data. Moreover,...
Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. In this work, we use various clustering approaches of data mining to analyse the crime data of Tamilnadu. The crime data is extracted from National Crime Records Bureau (NCRB) of India. It consists...
Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input...
Data clustering is a data analyzing technique that groups data based on the similarity. The similarities between the objects in the same group are high when data are well clustered and the similarities between objects in different groups are low. The data clustering technique is widely used in different areas such as bioinformatics, image segmentation and market research. All of the well-known clustering...
In today's world, where we generate large amount of data, we can harness the benefits of the hidden information i.e. patterns or correlations in these data. This information can be used in various constructive fields only if we are able to handle big data efficiently. One such process that is used to extract and handle the hidden information is data mining. There are various techniques in data mining...
The last years, huge bundles of information are extracted by computational systems and electronic devices. To exploit the derived amount of data, new innovative algorithms must be employed or the established ones maybe changed. One of the most fascinating and productive techniques, in order to locate and extract information from data repositories is clustering, and DBSCAN is a successful density based...
Social media analytics play a major role in e-commerce for extracting the useful information of a product or service. Opinion mining has become the key process of social media analytics. Twitter is a big online social activity platform where millions of people share their opinions. In this paper two clustering techniques, k-means and DBSCAN, are applied to an annotated Twitter dataset in order to...
In view of today's information available, recent progress in data mining research has lead to the development of various efficient methods for mining interesting patterns in large databases. It plays a vital role in knowledge discovery process by analyzing the huge data from various sources and summarizing it into useful information. It is helpful for analyzing the volumes of data in different domains...
Density based clustering technique, DBSCAN is used to extract knowledge and decision rules from large test data sets of a large steel truss bridge over river Brahmaputra. The analyses are based on variations of intra and inter cluster densities, shape of cluster distributions and total number of data points. DBSCAN parameters are optimized for obtaining a good clustering result and thereby identifying...
Clustering is a task that aims to grouping data objects into several groups. DBSCAN is a density-based clustering method. However, it requires two parameters and these two parameters are hard to decide. Also, DBSCAN has difficulties in finding clusters when the density changes in the dataset. In this paper, we modify the original DBSCAN to make it able to determine the appropriate eps values according...
Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract...
Clustering is an important tool which has seen an explosive growth in Machine Learning Algorithms. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is one of the most primary methods for clustering in data mining. DBSCAN has ability to find the clusters of variable sizes and shapes and it will also detect the noise. The two important parameters Epsilon (Eps)...
Uncertain data mining has recently attracted interests from researchers due to its presence in many applications such as Global Positioning System (GPS) Wireless Sensor Networks (WSN), Moving Object Tracking. This paper is researching uncertain data clustering problem, almost all the existed algorithms of uncertain data calculate expectation to express the distance of objects, so they can cluster...
Aiming at the problems existed in the process of mining manipulate errors for railway transport, an improved DBSCAN algorithm is presented. This method according to the monitoring data of operation of the train, mining railway transportation driver manipulate errors. Algorithm improved DBSCAN in dealing with boundary shared object of cluster by average distance function of shared object. As a result...
DBSCAN is a widely used technique for clustering in spatial databases. DBSCAN needs less knowledge of input parameters. Major advantage of DBSCAN is to identify arbitrary shape objects and removal of noise during the clustering process. Beside its familiarity, DBSCAN has problems with handling large databases and in worst case its complexity reaches to O(n2). Similarly, DBSCAN cannot produce correct...
Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-based clustering method called GBC which detects automatically the best number of clusters, without requiring any other parameter. In this method based on regions of influence, a graph is constructed and the edges of the...
Knowledge of wetland use of migratory bird species during the annual life circle is important to construct conservation strategy and explore the implication for avian influenza control. Biological scientists have used GPS satellite telemetry to determine the habitat of wild birds. However, because there is not an efficient method to process the location data sets, scientists have to devote themselves...
Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should...
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