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128 articles for “clustering algorithms”
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Remote Sensing Classification Based on Improve Ant-Miner Algorithm: A Case Study of Alwar, Rajasthan, India
Abstract: Earth Observation Satellite (EOS) image itself contains image ambiguity. Various conventional methods like minimum distance to mean or maximum likelihood image clustering algorithm do not meet the accuracy that have required by user in the virtue of cost-effective land use/land cover classification. In Ant colony optimization (ACO), association rule mining is a prevalent and well researched method for discovering useful relations between variables in large databases. The proposed work presents …
Published in Journal of Geotechnical Engineering · Vol. 2, Issue 3, 2015 · pp. 1–5 Read article
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Country Cluster Visualization Based on Agricultural Imports: Unsupervised Learning Approach
Abstract: The clustering algorithm used in this analysis makes it easier for policymakers to understand performance metrics. K-means clustering has been demonstrated to be a tool for analyzing countries' agricultural imports and a visualization tool for interpreting the results in this study. Authors have used agricultural imports from 190 nations from Knoema, a web-based open data platform. Cereals, meat, and coffee imports from 190 nations in 2016 are included in the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
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A Technical Survey on Different Clustering Techniques & Fuzzy Logic in Wireless Sensor Network
Abstract: Abstract—The WSN is a distinctive network and WSN includes sensors at enormous scale as well as comprises at least single base station. When building wireless sensor networks, there are critical issues that are appeared such as preserving energy and load balancing. Lifetime of a sensor network is improved by implementing andmitigating energy consumption. Amount of a choice of parameters such as quantity of nodes, strength and range of area as …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 4, Issue 3, 2017 · pp. 1–8 Read article
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Improving DBSCAN using Parallel Computing Approaches
Abstract: AbstractData mining has become the buzz word of computational research due to its enormous economical and social significance. Clustering is subset of data mining operations and is a type of learning using observation techniques. Clustering uses on unsupervised learning model and does not require any training data to generate the clustering model. Clustering enables grouping of similar and dissimilar type of data in separate groups. DBSCAN is on emerging and …
Published in Recent Trends in Parallel Computing · Vol. 6, Issue 2, 2019 · pp. 20–26 Read article
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A Machine Learning Approach for Investigating Spatial Structures between Spectral line Sources: Formaldehyde Absorptions versus Methanol Masers
Abstract: In this paper the authors present fascinating ideas on using machine learning algorithms to study connections in spatial distributions between observed spectral line sources. The method has been illustrated using observations of 4.8 GHz formaldehyde absorptions and 6.7 GHz methanol masers in the Galaxy. Both spectral line features have been well-observed in close associations with galactic star-formation regions, and we initiate this study to understand spatial connections between them. We …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 2, Issue 2, 2013 · pp. 1–11 Read article
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Topic Detection in Tweets using Hashtags
Abstract: Data clustering is a common technique used for topic detection and identification in text. In this paper we propose tokenizing of hashtags as an effective and efficient way of generating bags of words for tweets (text messages on Twitter). The document- term matrix generated using these terms is smaller and more relevant compared to the full text tokenization. Tweets from the 2018 Oscars event tagged with #oscars hashtag were collected …
Published in Recent Trends in Programming languages · Vol. 5, Issue 2, 2018 · pp. 6–14 Read article
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Plant Disease Detection Using SVM
Abstract: AbstractDiseases in plants are responsible for major production and economic losses and also reduction in yield. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself, it detects the symptoms of diseases. Detection on plant is very critical for defensible agriculture. It is very challenging to monitor the plant diseases physically. …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 4, Issue 3, 2017 · pp. 8–12 Read article
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Performance Analysis of BEEM using Fuzzy Logic in Wireless Sensor Network
Abstract: Advances in wireless sensor network technology provides the availability of small and low-cost sensor nodes which has capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. This sensing capability results in extremely large amount of application areas. However, the characteristics of wireless sensor networks require more effective method for data forwarding and processing. To upgrade the energy utilization, clustering is one of the prototypical …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 7, Issue 1, 2020 · pp. 31–38 Read article
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Mining WebLog Data Using Enhanced Map-Reduce Based SOM Algorithm
Abstract: Web utilization mining is a procedure for finding client route designs in web server to get web log data. These route designs are additionally scrutinized by different information mining strategies. The found route instances can be further used to recognize the successive transactions of the client, foreseeing the future demand of client, and so on which very much essential due to enormous developments in electronic trade sites.Through a colossal measure …
Published in Journal of Advanced Database Management & Systems · Vol. 5, Issue 2, 2018 · pp. 10–17 Read article
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A Novel Approach to Reduce Image Retrieval Problem Using Gabor Transform, Wavelet Transform and Euclidean Distance Measure
Abstract: In the processing of image, pattern recognition and computer vision, the image retrieval is a most famous research area. Our paper presents a new method in CBIR through merging the low level feature i.e. texture, color and shape features. At first, we transform the color space from RGB model to HSV model, and then extract color histogram to form color feature vector. CBIR is the process where search of image …
Published in E-Commerce for Future & Trends · Vol. 3, Issue 1, 2016 · pp. 10–15 Read article
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Extracting High Dimensional Data by Using Fast Clustering Based Feature Subset Selection System
Abstract: AbstractThis paper presents the feature assortment involve identifying a subset of the majority valuable features with the intention of produce companionable results the same as the inventive complete set of features. A feature assortment system could exist evaluate commencing both the effectiveness and efficiency points of analysis. Although the effectiveness concerns the point in time necessary in the direction of locate a subset of features, the efficiency is linked toward …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 3, Issue 3, 2016 · pp. 1–13 Read article
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Fast Data Analysis Technique by Compact Clustering
Abstract: In this paper, a new clustering theory is established under the context to analyze the data. Data analysis is a challenging task. The proposed method transforms it into easier. The classical clustering method defines over the probability to design the proposed algorithm in this paper. The data structure sets as the dynamic structure for mapping the optimized and compact analysis.Keywords: Data, Probability, Cluster, Data, Algorithm.Cite this Article: Sunil Kumar Kashyap, …
Published in Journal of Advanced Database Management & Systems · Vol. 7, Issue 2, 2020 · pp. 19–23 Read article
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Comparing UWDBCSN Clustering with LNCA for Node Replication Attack
Abstract: AbstractClustering in WSN divides sensor nodes into different clusters. Each node is headed by a cluster head. Instead of direct routing, the communication can take place between cluster heads that increases the lifetime of sensor network. Some security issues in clustering may arise because of the presence of attacker node(s) in the network. Attacker nodes may disrupt the whole cluster by introducing attacks like node replication, denial of service, wormhole, …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 4, Issue 2, 2017 · pp. 41–49 Read article
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An Empirical Study on Evaluating Graph Based Clustering for HD Data Using Attribute Selection
Abstract: An attribute subset selection can be showed as a process of identifying and eliminating or removing a number of irrelevant and surplus attributes (features) because irrelevant attributes do not give predictive accuracy and the surplus attributes provide the information that is already present in the other attributes. Attribute selection involves identifying a subset of the most useful attributes that produces the similar results as the final set of results. An …
Published in Journal of Advanced Database Management & Systems · Vol. 1, Issue 2, 2014 · pp. 33–40 Read article
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Enhanced Incremental DB Scan Algorithm using Map-Reduce
Abstract: Distributed data mining is more efficient, scalable and its performance is better than the central data mining techniques. Incremental DBSCAN algorithm is better than the other method DBSCAN. Incremental DBSCAN can give better performance in distributed environment in terms of run time complexity using Hadoop platform. The proposed system uses spatial dataset i.e. dataset are distributed among different sites. Initially, clusters are generated at each site using DBSCAN algorithm. Then …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 3, Issue 2, 2015 · pp. 1–6 Read article
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Model Based Clustering using Finite Mixture Models of Lognormal Distribution
Abstract: AbstractModel-based clustering consists of fitting a mixture model to data and identifying cluster with its components. This study discusses model-based clustering using a finite mixture of a univariate lognormal distribution with k components. In this study, lognormal mixture models for skewed data and also the estimation of mixing proportion by distance-based clustering have been considered. Parameter estimation is carried out by expectation maximization (EM) algorithm using different initialization approach using …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 58–67 Read article
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Hadoop and Map Reduce for Simplified Processing of Big Data
Abstract: Big data refers to large and complex data sets made up of a variety of structured and unstructured data, which are too big, too fast, or too hard to be managed by traditional techniques. Hadoop is an open source software platform for structuring big data on computer clusters built from commodity hardware and enabling it for data analysis. It is designed to scale up from a single server to thousands …
Published in Current Trends in Information Technology · Vol. 6, Issue 3, 2016 · pp. 1–6 Read article
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Asynchronous Check pointing Protocols for Cluster Based MANETs: A Comparative Analysis
Abstract: The recent advancement in the field of wireless networking and mobile computing has led to the development of variety of applications for wireless networks. Mobile Ad Hoc Networks (MANETs) are emerging as one of the major wireless technology in the field of mobile computing. A MANET is a group of mobile devices or nodes that are self-configurable and can communicate using wireless links. The nodes of such a network have …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 2, Issue 3, 2015 · pp. 19–25 Read article
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Enhancing Cybercrime Detection Through Big Data Analytics: A Conceptual Framework
Abstract: The growing complexity of cybercrime reduction and prevention difficulties necessitates a different approach to dealing with the massive amounts of data involved. The issues of reducing and preventing cybercrime are becoming more complex as cybercrime becomes ingrained in our daily lives. When it comes to showcasing the original division of criminal activities, traditional police operations typically fall short, which means they contribute less to the right deployment of police resources. …
Published in Journal Of Network security · Vol. 11, Issue 2, 2023 · pp. 1–11 Read article
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An Application of Recommendation Systems to Cluster-based Pattern Recognition
Abstract: Information overload is a common problem faced by internet users; so, the recommendation system is becoming increasingly important to this service. Collective filtering is used by recommendation systems to offer recommendations to individuals or for products based on their shared interests. Data or people should be grouped into clusters so that they have more in common with those in the same cluster and stand out from those in other clusters. …
Published in Recent Trends in Parallel Computing · Vol. 10, Issue 2, 2023 · pp. 28–36 Read article