An improved K-means algorithm combined with Particle Swarm Optimization approach for efficient web document clustering

Jaganathan, P.; Jaiganesh, S.

doi:10.1109/icgce.2013.6823538

Cited by 21 publications

(6 citation statements)

References 9 publications

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“…There have been lot of scholars who implemented K-means algorithm such as Chandhok et al [16], Yao et al [17] and Moftah et al [18] who successfully implemented K-means for image segmentation. Oyelade et al [19] implemented K-means algorithm for analyzing students data of a private Institution in Nigeria, while Jaganathan and Jaiganesh [20] used K-means algorithm in web document clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Educational Data Mining Techniques Approach to Predict Student’s Performance

Khasanah¹,

Harwati²

2019

IJIET

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Predicting student's performance is one way that can be conducted by university to monitor their student to prevent student failed. Student final GPA is one parameter that must be full fill by student to graduate from university and it can be used to measure student's performance. Educational Data Mining is popular techniques to predict student's performance. This study tried to implement two popular data mining clustering and classification analysis to predict student's performance. K-means algorithm is used since it is very popular and easy to be implemented clustering algorithm. Linear Regression and Support Vector Machine (SVM) then used to predict the final GPA since the attributes used in this study is numerical data. The clustered data and non-clustered data were evaluated in the classification analysis and the MSE was compared. The result showed that clustered data had smaller RMSE and Linear Regression was better than SVM.

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Section: Literature Reviewmentioning

confidence: 99%

Educational Data Mining Techniques Approach to Predict Student’s Performance

Khasanah¹,

Harwati²

2019

IJIET

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“…Various initial centroid (seed) selection methods are used to select the initial centroids for K -means clustering [11,16–26]. Most of them have focused on only one-dimensional data set.…”

Section: Introductionmentioning

confidence: 99%

DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering

Lakshmi

Baskar²

2018

Journal of Information Science

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In this article, a new initial centroid selection for a K-means document clustering algorithm, namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means (DIC-DOC- K-means), to improve the performance of text document clustering is proposed. The first centroid is the document having the minimum standard deviation of its term frequency. Each of the other subsequent centroids is selected based on the dissimilarities of the previously selected centroids. For comparing the performance of the proposed DIC-DOC- K-means algorithm, the results of the K-means, K-means++ and weighted average of terms-based initial centroid selection + K-means (Weight_Avg_Initials + K-means) clustering algorithms are considered. The results show that the proposed DIC-DOC- K-means algorithm performs significantly better than the K-means, K-means++ and Weight_Avg_Initials+ K-means clustering algorithms for Reuters-21578 and WebKB with respect to purity, entropy and F-measure for most of the cluster sizes. The cluster sizes used for Reuters-8 are 8, 16, 24 and 32 and those for WebKB are 4, 8, 12 and 16. The results of the proposed DIC-DOC- K-means give a better performance for the number of clusters that are equal to the number of classes in the data set.

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“…These roads are comparatively less difficult to extract than developed urban roads because of less. [3,4] The history of road extraction can be linked to the earlier year of 70s. When remote sensing and its applications came into use the need of road extraction was felt, since then researchers started working in the area of image processing [5].…”

Section: Introductionmentioning

confidence: 99%

Road Extraction using Connected Component Techniques

Jayaseeli¹,

Malathi²,

Karandikar³

et al. 2018

IJET

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Extraction of Roads, Rivers and other map objects is an important step in many military and civilian applications. In this process the information is extracted which possess high efficiency and accuracy and is fed into GIS (Geographical Information System). In this paper, we have explored different algorithms with better efficiency and accuracy. Road extraction can take place for two kinds of roads namely: urban and non-urban roads. Urban roads are more complex to analyze because of their architectural complexity, occlusions created by trees, heavy traffic and extensive network, whereas non-urban roads are easier to analyze because of less structural complexity. The proposed algorithm exploits the properties of road segments to develop customized operators to accurately derive the road segments. The customized operators include directional morphological enhancement, directional segmentation and thinning. The proposed algorithm is systematically evaluated on the basis of variety of images and compared with other algorithms (Canny, Sobel, Roberts, and Morphological Segmentation). The results demonstrate that the algorithm proposed is both accurate and efficient. The data and performance measures such as completeness and correctness are calculated together with other parameters which are Peak Signal to Noise ratio, Normalized Cross Correlation, Structural Content and a statistical analysis of the comparison is presented.

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An improved K-means algorithm combined with Particle Swarm Optimization approach for efficient web document clustering

Cited by 21 publications

References 9 publications

Educational Data Mining Techniques Approach to Predict Student’s Performance

Educational Data Mining Techniques Approach to Predict Student’s Performance

DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering

Road Extraction using Connected Component Techniques

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