Rough clustering of sequential data

Kumar, Pradeep; Krishna, P. Radha; Bapi, Raju S.; De, S. K.

doi:10.1016/j.datak.2007.01.003

Cited by 78 publications

(42 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make another comparative evaluation against the clustering method by (Hababeh 2012) in terms of the maximum load (bit/sec) which is generated from all nodes as an indicator of the consumed communication cost, we considered case of 10 nodes compared with (Hababeh 2012) and his most related work (Kumar et al 2007) and (Fronczak et al 2002). Figure 3 depicts the load of the clustering methods under comparison.…”

Section: # Nodes 10mentioning

confidence: 99%

“…This helps in discovering the access pattern which can be adapted later by each node (Jiawei & Micheline 2006;Kumar et al 2007). After discovering the access pattern, the clusters are generated based on the rule that the communication cost between two sites in the discovered pattern is less than the validity of the replicated data; i.e.…”

Section: The Cluster Componentmentioning

confidence: 99%

See 1 more Smart Citation

Scalable Data-Oriented Replication with Flexible Consistency in Real-Time Data Systems

Salem

Saleh

Abdul-Kader

2016

Data Science Journal

View full text Add to dashboard Cite

Section: # Nodes 10mentioning

confidence: 99%

Section: The Cluster Componentmentioning

confidence: 99%

Scalable Data-Oriented Replication with Flexible Consistency in Real-Time Data Systems

Salem

Saleh

Abdul-Kader

2016

Data Science Journal

View full text Add to dashboard Cite

“…The hierarchical clustering algorithm [14][15][16] is used in the study to cluster the data of network pubic opinion, and the advantage and disadvantage of clustering is evaluated on the basis of the purity index. After the text clustering, the purity index for the clustering r is defined as follows:…”

Section: A Text Clusteringmentioning

confidence: 99%

The Application of Gray Model and Support Vector Machine in the Forecast of Online Public Opinion

Xu¹

2013

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Abstract-The forecast of online public opinion is a kind of complex forecasting problem with information, small sample and uncertainty. In order to improve the accuracy for the forecast of online public opinion, a new forecasting method based on a gray model and a support vector machine is proposed. The method comprises the steps of clustering the text, extracting the hotspots, aggregating the data and implementing other pretreatments of the network data, then creating a model GM (1, 1) for the time series of online public opinion, correcting the forecasting results of the model GM (1, 1) with a support vector machine, and then testing through a simulation experiment. The experimental results show that compared with traditional forecasting methods, the application of gray model and support vector machine improves the accuracy for the forecast of online public opinion. Moreover, a new method for the forecast of online public opinion is presented to some extent.Index Terms-online public opinion, GM (1, 1), support vector machine, forecasting.

show abstract

“…The existing clustering methods are divided into hierarchical clustering [6]- [10] , partition clustering [11] - [29], and Clustering algorithm based on grid and density [30]- [34].…”

Section: Introductionmentioning

confidence: 99%

An Improved K-means Algorithm Based on Structure Features

Zhan¹

2017

JSW

View full text Add to dashboard Cite

Abstract:In K-means clustering, we are given a set of n data points in multidimensional space, and the problem is to determine the number k of clusters. In this paper, we present three methods which are used to determine the true number of spherical Gaussian clusters with additional noise features. Our algorithms take into account the structure of Gaussian data sets and the initial centroids. These three algorithms have their own emphases and characteristics. The first method uses Minkowski distance as a measure of similarity, which is suitable for the discovery of non-convex spherical shape or the clusters with a large difference in size. The second method uses feature weighted Minkowski distance, which emphasizes the different importance of different features for the clustering results. The third method combines Minkowski distance with the best feature factors. We experiment with a variety of general evaluation indexes on Gaussian data sets with and without noise features. The results showed that the algorithms have higher precision than traditional K-means algorithm.

show abstract

Rough clustering of sequential data

Cited by 78 publications

References 13 publications

Scalable Data-Oriented Replication with Flexible Consistency in Real-Time Data Systems

Scalable Data-Oriented Replication with Flexible Consistency in Real-Time Data Systems

The Application of Gray Model and Support Vector Machine in the Forecast of Online Public Opinion

An Improved K-means Algorithm Based on Structure Features

Contact Info

Product

Resources

About