Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System

2015 ◽  
Vol 2 (1) ◽  
pp. 70-87 ◽  
Author(s):  
Hannah Inbarani H ◽  
Selva Kumar S
Keyword(s):  
Web 2.0 ◽  
Clustering Algorithm ◽  
Social Systems ◽  
Social Tagging ◽  
Social Bookmarking ◽  
Swarm Optimization ◽  
Tolerance Rough Set ◽  
Clustering Approach ◽  
Tag Clustering ◽  
Tagging System

Social tagging is one of the vital attributes of WEB2.0. The challenge of Web 2.0 is a gigantic measure of information created over a brief time. Tags are broadly used to interpret and arrange the web 2.0 assets. Tag clustering is the procedure of grouping the comparable tags into clusters. The tag clustering is extremely valuable for researching and organizing the web2. 0 resources furthermore critical for the achievement of Social Bookmarking frameworks. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Firefly (TRS-Firefly-K-Means) clustering algorithm for clustering tags in social systems. At that stage, the proposed system is contrasted with the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis outlines the viability of the suggested methodology.

Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System

2015 ◽  
Vol 2 (1) ◽  
pp. 22-37 ◽  
Author(s):  
Hannah Inbarani H ◽  
Selva Kumar S ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien
Keyword(s):  
Particle Swarm Optimization ◽  
Web 2.0 ◽  
Clustering Algorithm ◽  
Social Systems ◽  
Particle Swarm ◽  
Social Tagging ◽  
Social Bookmarking ◽  
Swarm Optimization ◽  
Short Period ◽  
Tag Clustering

Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social systems. Then the proposed method is compared to the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis illustrates the effectiveness of the proposed approach.

Harnessing Web 2.0 for Context-Aware Learning

2010 ◽  
pp. 98-113 ◽  
Author(s):  
Wai-Tat Fu ◽  
Thomas Kannampallil
Keyword(s):  
Web 2.0 ◽  
Undergraduate Students ◽  
Distributed Cognition ◽  
Formal Model ◽  
Social Tagging ◽  
The Social ◽  
Formal And Informal Learning ◽  
Learning Data ◽  
Tagging System

We present an empirical study investigating how interactions with a popular social tagging system, called del.icio.us, may directly impact knowledge adaptation through the processes of concept assimilation and accommodation. We observed 4 undergraduate students over a period of 8 weeks and found that the quality of social tags and distributions of information content directly impact the formation and enrichment of concept schemas. A formal model based on a distributed cognition framework provides a good fit to the students learning data, showing how learning occurs through the adaptive assimilation of concepts and categories of multiple users through the social tagging system. The results and the model have important implications on how Web 2.0 technologies can promote formal and informal learning through collaborative methods.

Harnessing Web 2.0 for Context-Aware Learning

2010 ◽  
pp. 1511-1526
Author(s):  
Wai-Tat Fu ◽  
Thomas Kannampallil
Keyword(s):  
Web 2.0 ◽  
Undergraduate Students ◽  
Distributed Cognition ◽  
Formal Model ◽  
Social Tagging ◽  
The Social ◽  
Formal And Informal Learning ◽  
Learning Data ◽  
Tagging System

We present an empirical study investigating how interactions with a popular social tagging system, called del.icio.us, may directly impact knowledge adaptation through the processes of concept assimilation and accommodation. We observed 4 undergraduate students over a period of 8 weeks and found that the quality of social tags and distributions of information content directly impact the formation and enrichment of concept schemas. A formal model based on a distributed cognition framework provides a good fit to the students learning data, showing how learning occurs through the adaptive assimilation of concepts and categories of multiple users through the social tagging system. The results and the model have important implications on how Web 2.0 technologies can promote formal and informal learning through collaborative methods.

Collaborative Mediation

2012 ◽  
pp. 21-39
Author(s):  
Brian Goodman
Keyword(s):  
Web 2.0 ◽  
Collaborative Filtering ◽  
Side Effect ◽  
Social Systems ◽  
Social Spaces ◽  
User Experiences ◽  
System A ◽  
High Level ◽  
Digital Fingerprints ◽  
Tagging System

Individuals are the generators and consumers of content, and in doing so, make up a substantial presence in the literate internet, above and beyond the formal media outlets that make up the minority. Accelerating the explosion of content are Web 2.0 interactions, where participants are encouraged to engage with primary content. These social spaces are a platform, supporting often-overlooked micro-interactions referred to in this chapter as digital fingerprints. In parallel, companies construct web experiences that uniquely deliver Internet inspired experiences. However, the competition that divides popular Internet destinations is absent in well run intranets. Collaboration and cooperation among internal web properties offer a unique opportunity to organize people and information across disparate experiences. An example of such a solution is IBM’s Enterprise Tagging System, a collaborative classification and recommendation service that knits employee identities and destinations together through fingerprints. The benefit of creating such a common service also exhibits the side effect and power of the relative few participants. It introduces the desperate need to consider how actions and relationships affect user experiences. The success of social systems requires a high level of diverse participation. This diversity is what ensures the mediation and influence of co-creation and collaborative filtering is not overly narrow.

Rough Clustering Method Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 325-326 ◽  
pp. 1632-1636
Author(s):  
Chao Wang ◽  
Ke Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Rough Set Theory ◽  
Particle Swarm ◽  
Local Optimum ◽  
Clustering Methods ◽  
Swarm Optimization ◽  
Clustering Approach ◽  
Definite Limit

As a relatively novel clustering approach, Particle Swarm Optimization (PSO) prevents k-means algorithm from falling into local optimum effectively, and has made relatively notable successes in clustering, however, using Hard C-Means algorithm when randomly obtaining initial clustering centers is required in most existing PSOs, while no definite limit existing in these samples actually. Based on this, we utilized an improved PSO; along with effective processing methods on boundary objects of Rough Set Theory, we proposed a new rough clustering algorithm based on PSO. It can adjust the upper and lower approximations weighting factors dynamically, and coordinate the proportions of upper and lower approximations in different generations as well. Finally, we compared it with several common clustering methods using Iris dataset of UCI. It turned out that the algorithm has higher accuracy and stability, along with better comprehensive performance.

scholarly journals Fractional Fuzzy Clustering and Particle Whale Optimization-Based MapReduce Framework for Big Data Clustering

2019 ◽  
Vol 29 (1) ◽  
pp. 1496-1513 ◽  
Author(s):  
Omkaresh Kulkarni ◽  
Sudarson Jena ◽  
C. H. Sanjay
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mapreduce Framework ◽  
Swarm Optimization ◽  
Skin Segmentation ◽  
Kernel Clustering ◽  
Whale Optimization ◽  
Clustering Approach

Abstract The recent advancements in information technology and the web tend to increase the volume of data used in day-to-day life. The result is a big data era, which has become a key issue in research due to the complexity in the analysis of big data. This paper presents a technique called FPWhale-MRF for big data clustering using the MapReduce framework (MRF), by proposing two clustering algorithms. In FPWhale-MRF, the mapper function estimates the cluster centroids using the Fractional Tangential-Spherical Kernel clustering algorithm, which is developed by integrating the fractional theory into a Tangential-Spherical Kernel clustering approach. The reducer combines the mapper outputs to find the optimal centroids using the proposed Particle-Whale (P-Whale) algorithm, for the clustering. The P-Whale algorithm is proposed by combining Whale Optimization Algorithm with Particle Swarm Optimization, for effective clustering such that its performance is improved. Two datasets, namely localization and skin segmentation datasets, are used for the experimentation and the performance is evaluated regarding two performance evaluation metrics: clustering accuracy and DB-index. The maximum accuracy attained by the proposed FPWhale-MRF technique is 87.91% and 90% for the localization and skin segmentation datasets, respectively, thus proving its effectiveness in big data clustering.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

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