Clustering Analysis and Its Applications

Lee, R. C. T.

doi:10.1007/978-1-4613-9883-7_4

Cited by 55 publications

(17 citation statements)

References 55 publications

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“…Then so the clusters become: Group 1 = {(3,4)(2,6)(3,8)(4,7)} Group 2 = {(7,4)(6,2)(6,4)(7,3)(8,5)(7,6)} Since the points (2,6) (3,8) and (4,7) are close to c 1 hence they form one cluster whilst remaining points form another cluster. So the total cost involved is 20.…”

Section: K-medoidmentioning

confidence: 99%

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Comparison between K-Means and K-Medoids Clustering Algorithms

Madhulatha¹

2011

Communications in Computer and Information Science

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Abstract. Clustering is a common technique for statistical data analysis, Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets according to some defined distance measure. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. It is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. In this research, the most representative algorithms K-Means and K-Medoids were examined and analyzed based on their basic approach.

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Section: K-medoidmentioning

confidence: 99%

“…Total Cost={ cost((3,4),(2,6))+ cost((3,4), (3,8))+ cost((3,4),(4,7))} + {cost((7,4),(6,2))+ cost((7,4),(6,4))+ cost((7,4),(7,3)) + cost((7,4), (8,5)) + cost((7,4),(7,6))} =20…”

Section: K-medoidmentioning

confidence: 99%

Comparison between K-Means and K-Medoids Clustering Algorithms

Madhulatha¹

2011

Communications in Computer and Information Science

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“…In clustering, the focus is on finding a partition of data records into clusters such that the points within each cluster are close to one another. It can also be defined as a process which partitions a set of data (or objects) into a set of meaningful sub-classes called clusters [8].…”

Section: Clusteringmentioning

confidence: 99%

Role of Data Mining Techniques in Agriculture Improvement

Sindhu¹,

Sindhu²

2017

International Journal of Advanced Research in Computer and Comm

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Data mining is the process of discovering and extracting of interesting patterns and knowledge from large amounts of data. The field of agriculture has to deal with large amounts of data and processing and retrieval of significant data from this abundance of agricultural information is necessary to help the farmers. Therefore, appropriate methods and techniques are required for managing and organizing this data to increase the efficiency and agricultural productivity. The application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel approach in agriculture. Data mining can help to process and convert this raw data into useful information for improving agriculture. In this paper, various data mining techniques used for processing of agricultural information/data such as k-means clustering, k-nearest neighbour, artificial neural networks, support vector machine, naive Bayesian classifier and fuzzy c-means are described. With the advancement of novel and appropriate data mining techniques, different types of agricultural problems will be addressed to improve crop productivity.

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“…We have included two border regions in the GKU for recording missing values and data with out of range values. In these borders, different regions are defined to identify the nature of the missing or out of range data ( (3) x > xmax at y = y; (4) x > xmax and y < ymin; (5) y < ymin at x = x; (6) x < xmin and y < ymin; (7) x < xmin at y = y; (8) x < xmin and y > ymax; (9) y is missing and x < xmin; (10) y is missing at x = x; (11) y is missing and x > xmax; (12) both x and y are missing; (13) x is missing and y > ymax; (14) x is missing at y = y; and (15) x is missing and y < ymin. * Shading is used in the figure to highlight different areas.…”

Section: Visualization Of Missing and Out Of Range Valuesmentioning

confidence: 99%

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

et al. 2016

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Big data are visually cluttered by overlapping data points. Rather than removing, reducing or reformulating overlap, we propose a simple, effective and powerful technique for density cluster generation and visualization, where point marker (graphical symbol of a data point) overlap is exploited in an additive fashion in order to obtain bitmap data summaries in which clusters can be identified visually, aided by automatically generated contour lines. In the proposed method, the plotting area is a bitmap and the marker is a shape of more than one pixel. As the markers overlap, the red, green and blue (RGB) colour values of pixels in the shared region are added. Thus, a pixel of a 24-bit RGB bitmap can code up to 2 24 (over 1.6 million) overlaps. A higher number of overlaps at the same location makes the colour of this area identical, which can be identified by the naked eye. A bitmap is a matrix of colour values that can be represented as integers. The proposed method updates this matrix while adding new points. Thus, this matrix can be considered as an up-to-time knowledge unit of processed data. Results show cluster generation, cluster identification, missing and out-of-range data visualization, and outlier detection capability of the newly proposed method.

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Clustering Analysis and Its Applications

Cited by 55 publications

References 55 publications

Comparison between K-Means and K-Medoids Clustering Algorithms

Comparison between K-Means and K-Medoids Clustering Algorithms

Role of Data Mining Techniques in Agriculture Improvement

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

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