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Recently, some researchers attempt to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of them can accurately deduce a time point when social media activities are highly affected by a rare event. Thus, it is difficult to characterize an...
Topological data analysis is a noble method to analyze high-dimensional qualitative data using a set of properties from topology. In this paper, we explore the feasibility of topological data analysis for mining social media data by investigating the problem of image popularity. We randomly crawl images from Instagram, convert their captions to 300 dimensional numerical vectors using Word2vec, calculate...
Millions of users create user profiles on social media. Changes made to an attribute in the user profiles on social media generate a huge volume of data representing a data stream. A framework has been proposed to analyze such data streams and cluster the attribute values related to each other.
People generate online content everyday at every hour at social networks. Social networks are a medium in which people can give their opinion on different topics and obtain new information. The content people create can be useful for researchers to understand human behavior in cities such as Quito. In this work, we are going to describe how Quito city is described on the travel network TripAdvisor...
Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion...
A persona in a social network is defined as the person's activities and attributes in a social network as seen by others. And a community in a social network is defined as a group of users in that social network which share common interests and are most likely to interact with each other in the network. For community detection, a user's persona and its connections with the other users in a network,...
Recently, opinion leader discovery has drawn much attention due to its widespread applicability. By identifying the opinion leader, companies or governments can manipulate the selling or guiding public opinion, respectively. However, mining opinion leader is a challenge task because of the complexity of processing social graph and analyzing leadership quality. In this study, a novel method, TCOL-Miner,...
Now a day Social media communication become to important factor for business operation. Several Customer prefers to post their comment, suggestion, complaints about company's products and services to online media such as Facebook, Twitter, Social web board because it easy way to blast to public and increases pressure to product owner for responding. This is one factor that cooperate need to be concern...
Data clustering is a data analyzing technique that groups data based on the similarity. The similarities between the objects in the same group are high when data are well clustered and the similarities between objects in different groups are low. The data clustering technique is widely used in different areas such as bioinformatics, image segmentation and market research. All of the well-known clustering...
We propose a new version of an algorithm based on Dirichlet boundary for community detection under the assumption of partially pre-labelled community members. We present a complete mathematical derivation of this method from the continuous Dirichlet boundary problem making explicit various assumptions underlying its discretization. We show, based on this derivation, how our algorithm differs from...
Currently, network perspective is rapidly becoming trends for representing and analyzing problems across all of the domains from natural science to engineering and management, with no exception in supply chain management. Treating supply chain system as a network give a good advantages since there are a lot of method in network theory that can be applied to give a quantitative measurement. In turns,...
Prime intends of web mining is to mine valuable information and knowledge from web. Social network analysis has become a very well-liked field of research as it is functional for many applications. In this study we will examine the existing soft computing techniques in the area of web mining. We develop efficient methods and algorithms using soft computing approaches. Our Framework will base on Hybrid...
In this paper, we investigate a typical clustering technology, namely, Gaussian mixture model (GMM)-based approach, for user interest prediction in social networks. The establishment of the model follows the following process: collect dataset from 4613 users and more than 16 million messages from Sina Weibo, obtain each user's interest eigenvalue sequence and establish GMM model to clustering users...
Given a set of n entities to be classified, and a matric of dissimilarities between pairs of them. This paper considers the problem called Minimum Sum of Diameters Clustering Problem, where a partition of the set of entities into k clusters such that the sum of the diameters of these clusters is minimized. Brucker showed that the complexity of the problem is NP-hard, when k ≥ 3 [1]. For the case of...
The paper identifies the scope of improvement for the search result of a web site. The study includes some commonly used clustering algorithms to identify the usage of clustering approach for improving web elements analysis, in various ways. As the Search result option is extensively used at almost every web site, the main focus is to optimize search result of a web site using clustering approach...
Methods for clustering static graphs cannot always be transferred straight forward to dynamic scenarios. A typical approach is to reduce the number of updates by reusing results of previous iterations. But are there natural ways to implement dynamic graph clustering? This paper proposes a method, which was derived by graph based ant colony algorithms. Similar to other clustering algorithms, multiple...
In this paper scalable and parallelized method for cluster analysis based on random walks is presented. The aim of the algorithm introduced in this paper is to detect dense sub graphs (clusters) and sparse sub graphs (bridges) which are responsible for information spreading among found clusters. The algorithm is sensitive to vertices assignment uncertainty. It distinguishes groups of nodes which form...
The proliferation of location-acquisition devices and thriving development of social websites enable analyzing users' movement behaviors and detecting social events in dynamic trajectory streams. In this paper, we firstly analyze the challenges in trajectory stream clustering, and then depict a three-part framework to deal with this issue, that includes i) trajectory data pre-processing for higher...
In view of the problems existing in traditional recommendation algorithm of low accuracy and low efficiency, this paper presents a machine learning based social media recommendation algorithm. The algorithm is based on the traditional personalized collaborative filtering algorithm, and combines with the correlation characteristics among users in a social network. Besides, the algorithm also considers...
Discovering topologies in a social network targets various business applications such as finding key influencers in a network, recommending music movies in virtual communities, finding active groups in network and promoting a new product. Since social networks are large in size, discovering topologies from such networks is challenging. In this paper, we present a scalable topology discovery approach...
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