In this study, regional (cities, towns and villages
) data and tweet data are obtained from Twitter, an
d
extract information of "purchase information (Where
and what bought)" from the tweet data by
morphological analysis and rule-based dependency an
alysis. Then, the "The regional information" and th
e
"Theinformation of purchase history (Where and wha
t bought information)" are captured as bipartite
graph, and Responsiveness Pair Clustering analysis
(a clustering using correspondence analysis as
similarity measure) is conducted. In this study, si
nce it was found to be difficult to analyze a netwo
rk such
as bipartite graph having limitations in links by u
sing modularity Q, responsiveness is used instead o
f
modularity Q as similarity measure. As a result of
this analysis, "regional information cluster" which
refers
to similar "Theinformation of purchase history" nod
es group is generated. Finally, similar regions are
visualized by mapping the regional information clus
ter on the map. This visualization system is expect
ed to
contribute as an analytical tool for customers’ pur
chasing behaviour and so on.
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
Community detection of political blogs network based on structure-attribute g...IJECEIAES
Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.
Centrality Prediction in Mobile Social NetworksIJERA Editor
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, we propose a new method to predict centrality values of nodes in a dynamic environment. The proposed method is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities. The performance of the proposed method is evaluated through simulated predictions on data sets from real mobile networks. Results indicate significantly low prediction error rate occurs, with a suitable implementation of the proposed method.
Re-Mining Association Mining Results Through Visualization, Data Envelopment ...ertekg
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/re-mining-association-mining-results-through-visualization-data-envelopment-analysis-and-decision-trees/
Re-mining is a general framework which suggests the execution of additional data mining steps based on the results of an original data mining process. This study investigates the multi-faceted re-mining of association mining results, develops and presents a practical methodology, and shows the applicability of the developed methodology through real world data. The methodology suggests re-mining using data visualization, data envelopment analysis, and decision trees. Six hypotheses, regarding how re-mining can be carried out on association mining results, are answered in the case study through empirical analysis.
one of the areas of discrete mathematics is graph theory. From a pure mathematics viewpoint, graph theory studies the pairwise relationships between objects. Those objects are vertices. Graph theory is frequently applied to analysing relationships between objects. It is a natural extension of graph theory to apply that mathematical tool to the evaluation of forensic evidence. In fact the literature reveals several, limited, forensic applications of graph theory. The current paper describes a more broad based application of graph theory to the problem of evaluation relationships in forensic investigation. The process takes standard graph theory and identifies entities in the investigation as vertices with the connections between the various entities as edges. Those entities can be suspects, victims, computer system, or any entity relevant to the investigation. Regardless of the nature of the entity, all entities are represented as vertices, and the relationship between them is represented as edges connecting the vertices. This allows the mathematical modelling of the events in question and facilitates analysis of the data.
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
Community detection of political blogs network based on structure-attribute g...IJECEIAES
Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.
Centrality Prediction in Mobile Social NetworksIJERA Editor
By analyzing evolving centrality roles using time dependent graphs, researchers may predict future centrality values. This may prove invaluable in designing efficient routing and energy saving strategies and have profound implications on evolving social behavior in dynamic social networks. In this paper, we propose a new method to predict centrality values of nodes in a dynamic environment. The proposed method is based on calculating the correlation between current and past measure of centrality for each corresponding node, which is used to form a composite vector to represent the given state of centralities. The performance of the proposed method is evaluated through simulated predictions on data sets from real mobile networks. Results indicate significantly low prediction error rate occurs, with a suitable implementation of the proposed method.
Re-Mining Association Mining Results Through Visualization, Data Envelopment ...ertekg
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/re-mining-association-mining-results-through-visualization-data-envelopment-analysis-and-decision-trees/
Re-mining is a general framework which suggests the execution of additional data mining steps based on the results of an original data mining process. This study investigates the multi-faceted re-mining of association mining results, develops and presents a practical methodology, and shows the applicability of the developed methodology through real world data. The methodology suggests re-mining using data visualization, data envelopment analysis, and decision trees. Six hypotheses, regarding how re-mining can be carried out on association mining results, are answered in the case study through empirical analysis.
one of the areas of discrete mathematics is graph theory. From a pure mathematics viewpoint, graph theory studies the pairwise relationships between objects. Those objects are vertices. Graph theory is frequently applied to analysing relationships between objects. It is a natural extension of graph theory to apply that mathematical tool to the evaluation of forensic evidence. In fact the literature reveals several, limited, forensic applications of graph theory. The current paper describes a more broad based application of graph theory to the problem of evaluation relationships in forensic investigation. The process takes standard graph theory and identifies entities in the investigation as vertices with the connections between the various entities as edges. Those entities can be suspects, victims, computer system, or any entity relevant to the investigation. Regardless of the nature of the entity, all entities are represented as vertices, and the relationship between them is represented as edges connecting the vertices. This allows the mathematical modelling of the events in question and facilitates analysis of the data.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Enhance The Technique For Searching Dimension Incomplete Databasespaperpublications3
Abstract: Data ambiguity is major problem in the information retrieval ambiguity is due to the loss in the data dimension it causes lot of problem in various real life application. Database may incomplete due to missing dimension and value. In previous work is totally based on the missing value. We focus on the problem is to find the missing dimension in our work. Missing dimension leads towards the problem in the traditional query approach. Missing dimension information create computational problem, so large number of possible combinations of missing dimensions need to be examined to check similarity between the query object and the data objects . Our aim is to reduce the all recovery version to increase the system performance as number of possible recovery data is reduces the time to estimate the true result is also reduces. Keywords: Missing Dimensions, Similarity search, Whole sequence query, Probability triangle inequality, Temporal data.
Title: Enhance The Technique For Searching Dimension Incomplete Databases
Author: Mr. Amol Patil, Prof. Saba Siraj, Miss. Ashwini Sagade
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
A STUDY ON SIMILARITY MEASURE FUNCTIONS ON ENGINEERING MATERIALS SELECTION cscpconf
While designing a new type of engineering material one has to search for some existing
materials which suits design requirement and then he can try to produce new kind of
engineering material. This selection process itself is tedious as he has to select few numbers of
materials out of a set of lakhs of materials. Therefore in this paper a model is proposed to select
a particular material which suits the user requirement, by using some similarity/distance
measuring functionalities. Here thirteen different types of similarity/distance measuring
functionalities are examined. Performance Index Measure(PIM) is calculated to verify the
relative performance of the selected material with the target material. Then all the results are
normalised for the purpose of analysing the results. Hence the proposed model reduces the
wastage of time in selection and also avoids the haphazardly selection of the materials in materials design and manufacturing industries.
Recent Trends in Incremental Clustering: A ReviewIOSRjournaljce
This paper presents a review on recent trends in incremental clustering algorithms. It tries to focus on both clustering based on similarity measure and clustering not based on similarity measure. In this context, the paper is devoted to various typical incremental clustering algorithms. Mainly optimization, genetic and fuzzy approaches of these algorithms is covered in the paper. The paper is original with respect to one aspect that is, it provides a complete overview that is fully devoted to evolutionary algorithms for incremental clustering. A number of references are provided that describe applications of evolutionary algorithms for incremental clustering in different domains, such as human activity detection, online fault detection, information security, track an object consistently throughout the network solving boundary problem etc.
EVOLUTIONARY CENTRALITY AND MAXIMAL CLIQUES IN MOBILE SOCIAL NETWORKSijcsit
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
Social Network Mining has been an area of interesting research due to billions of people using social media. Community detection is identified as one of the major issues of a social network. Here, a new approach has been presented for community detection which is greedy as well as incremental in nature. The approach is tested on standard datasets and the results are presented as well as analyzed
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ABSTRACT
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
Effectual citizen relationship management with data mining techniqueseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggre...rahulmonikasharma
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques for data stream. We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining.
Dynamic extraction of key paper from the cluster using variance values of cit...IJDKP
When looking into recent research trends in the field of academic landscape, citation network analysis is
common and automated clustering of many academic papers has been achieved by making good use of
various techniques. However, specifying the features of each area identified by automated clustering or
dynamically extracted key papers in each research area has not yet been achieved. In this study, therefore,
we propose a method for dynamically specifying the key papers in each area identified by clustering. We
will investigate variance values of the publication year of the cited literature and calculate each cited
paper’s importance by applying the variance values to the PageRank algorithm.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Enhance The Technique For Searching Dimension Incomplete Databasespaperpublications3
Abstract: Data ambiguity is major problem in the information retrieval ambiguity is due to the loss in the data dimension it causes lot of problem in various real life application. Database may incomplete due to missing dimension and value. In previous work is totally based on the missing value. We focus on the problem is to find the missing dimension in our work. Missing dimension leads towards the problem in the traditional query approach. Missing dimension information create computational problem, so large number of possible combinations of missing dimensions need to be examined to check similarity between the query object and the data objects . Our aim is to reduce the all recovery version to increase the system performance as number of possible recovery data is reduces the time to estimate the true result is also reduces. Keywords: Missing Dimensions, Similarity search, Whole sequence query, Probability triangle inequality, Temporal data.
Title: Enhance The Technique For Searching Dimension Incomplete Databases
Author: Mr. Amol Patil, Prof. Saba Siraj, Miss. Ashwini Sagade
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
A STUDY ON SIMILARITY MEASURE FUNCTIONS ON ENGINEERING MATERIALS SELECTION cscpconf
While designing a new type of engineering material one has to search for some existing
materials which suits design requirement and then he can try to produce new kind of
engineering material. This selection process itself is tedious as he has to select few numbers of
materials out of a set of lakhs of materials. Therefore in this paper a model is proposed to select
a particular material which suits the user requirement, by using some similarity/distance
measuring functionalities. Here thirteen different types of similarity/distance measuring
functionalities are examined. Performance Index Measure(PIM) is calculated to verify the
relative performance of the selected material with the target material. Then all the results are
normalised for the purpose of analysing the results. Hence the proposed model reduces the
wastage of time in selection and also avoids the haphazardly selection of the materials in materials design and manufacturing industries.
Recent Trends in Incremental Clustering: A ReviewIOSRjournaljce
This paper presents a review on recent trends in incremental clustering algorithms. It tries to focus on both clustering based on similarity measure and clustering not based on similarity measure. In this context, the paper is devoted to various typical incremental clustering algorithms. Mainly optimization, genetic and fuzzy approaches of these algorithms is covered in the paper. The paper is original with respect to one aspect that is, it provides a complete overview that is fully devoted to evolutionary algorithms for incremental clustering. A number of references are provided that describe applications of evolutionary algorithms for incremental clustering in different domains, such as human activity detection, online fault detection, information security, track an object consistently throughout the network solving boundary problem etc.
EVOLUTIONARY CENTRALITY AND MAXIMAL CLIQUES IN MOBILE SOCIAL NETWORKSijcsit
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
Social Network Mining has been an area of interesting research due to billions of people using social media. Community detection is identified as one of the major issues of a social network. Here, a new approach has been presented for community detection which is greedy as well as incremental in nature. The approach is tested on standard datasets and the results are presented as well as analyzed
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
ABSTRACT
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
Effectual citizen relationship management with data mining techniqueseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Novel Multi- Viewpoint based Similarity Measure for Document ClusteringIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggre...rahulmonikasharma
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques for data stream. We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining.
Dynamic extraction of key paper from the cluster using variance values of cit...IJDKP
When looking into recent research trends in the field of academic landscape, citation network analysis is
common and automated clustering of many academic papers has been achieved by making good use of
various techniques. However, specifying the features of each area identified by automated clustering or
dynamically extracted key papers in each research area has not yet been achieved. In this study, therefore,
we propose a method for dynamically specifying the key papers in each area identified by clustering. We
will investigate variance values of the publication year of the cited literature and calculate each cited
paper’s importance by applying the variance values to the PageRank algorithm.
GRAPH ALGORITHM TO FIND CORE PERIPHERY STRUCTURES USING MUTUAL K-NEAREST NEIG...ijaia
Core periphery structures exist naturally in many complex networks in the real-world like social,
economic, biological and metabolic networks. Most of the existing research efforts focus on the
identification of a meso scale structure called community structure. Core periphery structures are another
equally important meso scale property in a graph that can help to gain deeper insights about the
relationships between different nodes. In this paper, we provide a definition of core periphery structures
suitable for weighted graphs. We further score and categorize these relationships into different types based
upon the density difference between the core and periphery nodes. Next, we propose an algorithm called
CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from
weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN).
Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed
core periphery structures.
Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neig...gerogepatton
Core periphery structures exist naturally in many complex networks in the real-world like social,
economic, biological and metabolic networks. Most of the existing research efforts focus on the
identification of a meso scale structure called community structure. Core periphery structures are another
equally important meso scale property in a graph that can help to gain deeper insights about the
relationships between different nodes. In this paper, we provide a definition of core periphery structures
suitable for weighted graphs. We further score and categorize these relationships into different types based
upon the density difference between the core and periphery nodes. Next, we propose an algorithm called
CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from
weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN).
Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed
core periphery structures.
Graph Algorithm to Find Core Periphery Structures using Mutual K-nearest Neig...gerogepatton
Core periphery structures exist naturally in many complex networks in the real-world like social, economic, biological and metabolic networks. Most of the existing research efforts focus on the identification of a meso scale structure called community structure. Core periphery structures are another equally important meso scale property in a graph that can help to gain deeper insights about the relationships between different nodes. In this paper, we provide a definition of core periphery structures suitable for weighted graphs. We further score and categorize these relationships into different types based upon the density difference between the core and periphery nodes. Next, we propose an algorithm called CP-MKNN (Core Periphery-Mutual K Nearest Neighbors) to extract core periphery structures from weighted graphs using a heuristic node affinity measure called Mutual K-nearest neighbors (MKNN). Using synthetic and real-world social and biological networks, we illustrate the effectiveness of developed core periphery structures.
An experimental evaluation of similarity-based and embedding-based link predi...IJDKP
The task of inferring missing links or predicting future ones in a graph based on its current structure
is referred to as link prediction. Link prediction methods that are based on pairwise node similarity
are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features,
and thus support the subsequent link prediction task in graph. This paper studies a selection of
methods from both categories on several benchmark (homogeneous) graphs with different properties
from various domains. Beyond the intra and inter category comparison of the performances of the
methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)-
based methods and heuristic ones as a means to alleviate the black-box well-known limitation
An experimental evaluation of similarity-based and embedding-based link predi...IJDKP
The task of inferring missing links or predicting future ones in a graph based on its current structure
is referred to as link prediction. Link prediction methods that are based on pairwise node similarity
are well-established approaches in the literature and show good prediction performance in many realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features,
and thus support the subsequent link prediction task in graph. This paper studies a selection of
methods from both categories on several benchmark (homogeneous) graphs with different properties
from various domains. Beyond the intra and inter category comparison of the performances of the
methods, our aim is also to uncover interesting connections between Graph Neural Network(GNN)-
based methods and heuristic ones as a means to alleviate the black-box well-known limitation.
Online Social Networks have become a prominent mode of communication and collaboration. Link Prediction is a major issue in Social Networks. Though ample methods are proposed to solve it, most of them take a static view of the network. Social Networks are dynamic in nature, this aspect has to be accounted. In this paper we propose a novel predictor LCF for Link Prediction in dynamic networks. In this method we view Social Networks as sequence of snapshots, each snapshot is the state of the network of a particular time period. Each edge of the network is assigned a weight based on its time stamp. We compute the LCF score for all node pairs in the network to predict the associations that may occur at a future time in the Social Network. We have also shown that our predictor outperforms the standard baseline methods for Link Prediction
The world has many natural systems that are so complex to be understood easily. This creates a need to have simple
principles or systems that capture the complexity of the world. The simple systems make it easier for many people to
understand the world by representing the complex world in a more straightforward way (Stefan, 2003). Many objects
and projects are seen to be a network of processes or substances. Graphs and networks have been used widely in
different projects for different reasons by project managers mostly. There are techniques such as critical path analysis
that make use of graphs and networks and are applied by project managers and all the staff involved in projects. These
methods are used to ensure smooth planning and control of projects. However, the techniques have to be applied
correctly to achieve the desired objective. This paper looks at the impact of graphs and networks in minimizing the costs
of a project or product. From this research, it can be inferred that the techniques such as critical path method, that make
use of graphs and networks, play a significant role in determining and hence reducing the product cost. This is done by
making the right decisions regarding the resources and time most appropriate for a project. The paper shows clearly
how these techniques are applied in a project to determine project duration and hence minimize the cost.
Sub-Graph Finding Information over Nebula Networksijceronline
Social and information networks have been extensively studied over years. This paper studies a new query on sub graph search on heterogeneous networks. Given an uncertain network of N objects, where each object is associated with a network to an underlying critical problem of discovering, top-k sub graphs of entities with rare and surprising associations returns k objects such that the expected matching sub graph queries efficiently involves, Compute all matching sub graphs which satisfy "Nebula computing requests" and this query is useful in ranking such results based on the rarity and the interestingness of the associations among nebula requests in the sub graphs. "In evaluating Top k-selection queries, "we compute information nebula using a global structural context similarity, and our similarity measure is independent of connection sub graphs". We need to compute the previous work on the matching problem can be harnessed for expected best for a naive ranking after matching for large graphs. Top k-selection sets and search for the optimal selection set with the large graphs; sub graphs may have enormous number of matches. In this paper, we identify several important properties of top-k selection queries, We propose novel top–K mechanisms to exploit these indexes for answering interesting sub graph queries efficiently.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Similar to The study about the analysis of responsiveness pair clustering tosocial network bipartite graph (20)
Advanced Computing: An International Journal (ACIJ) is a peer-reviewed, open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and a practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of computing.
Call for Papers - Advanced Computing An International Journal (ACIJ) (2).pdfacijjournal
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Submission Deadline : April 08, 2023
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline
Advanced Computing: An International Journal (ACIJ
)
is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advancedcomputing. The journal focuses on all technical and practical aspects of high performancecomputing, green computing, pervasive computing, cloud computing etc. The goal of this journalis to bring together researchers anda practitioners from academia and industry to focus onunderstanding advances in computing and establishing new collaborations in these areas
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline.com
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Discussed the need for a universal desktop and mobile app that can be used by immigration authorities and consulates all over the world to enable fast checking of passports and visas at ports of entry for forgery and fabrication.
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The study about the analysis of responsiveness pair clustering tosocial network bipartite graph
1. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
THE STUDY ABOUT THE ANALYSIS OF
RESPONSIVENESS PAIR CLUSTERING TOSOCIAL
NETWORK BIPARTITE GRAPH
Akira Otsuki 1 and Masayoshi Kawamura2
1
Tokyo Institute of Technology, Tokyo, Japan
2
MK future software, Ibaraki, Japan
ABSTRACT
In this study, regional (cities, towns and villages) data and tweet data are obtained from Twitter, and
extract information of "purchase information (Where and what bought)" from the tweet data by
morphological analysis and rule-based dependency analysis. Then, the "The regional information" and the
"Theinformation of purchase history (Where and what bought information)" are captured as bipartite
graph, and Responsiveness Pair Clustering analysis (a clustering using correspondence analysis as
similarity measure) is conducted. In this study, since it was found to be difficult to analyze a network such
as bipartite graph having limitations in links by using modularity Q, responsiveness is used instead of
modularity Q as similarity measure. As a result of this analysis, "regional information cluster" which refers
to similar "Theinformation of purchase history" nodes group is generated. Finally, similar regions are
visualized by mapping the regional information cluster on the map. This visualization system is expected to
contribute as an analytical tool for customers’ purchasing behaviour and so on.
KEYWORDS
Big Data analysis, customers’ purchasing behaviour analysis, Data Mining, Database
1. INTRODUCTION
"Big data" is not just meaning the size of the capacity simply.For example, it is real-time data or
nonstructural data, like the SNS (Social Networking Service) data.Big data is being used in
various fields as environmental biology [1], physics simulation [2], seismology, meteorology,
economics, and management information science.
Foreign countriesare making efforts aggressive towards big data utilization already according to
the data of Ministry of Internal Affairs and Communications [3].OSTP (Office of Science and
Technology Policy) in the US government released the "Big Data Research and Development
Initiative" at March2013.Other hand,FI-PPP (Future Internet Public-Private Partnership) program
is being implemented by EU at 2011. FI-PPP is a Public-Private-Partnership programme for
Internet-enabled innovation.In this manner, the study of Big Data is being implemented actively
worldwide as a recent trend. Therefore, it is conceivable that the study of Big Data is very
important.
We[4-6] did propose many Big Data analysis methods thus far. These are the methods based on
Bibliometrics and clustering (Newman Method).We will do Study about the Analysis of
"Responsiveness Pair Clustering" of Bipartite Graph of Regional/Purchase Big Data in this
paper.Concretely, first will get the Regional (Ex: cities, towns and villages)data and purchase data
from Twitter. Next,will do "Responsiveness Pair Clustering Analysis" about bipartite graph of
DOI : 10.5121/acij.2013.4601
1
2. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
regional and purchase data.Although former clustering method used the structure of the link as
Although
the similarity measure of clustering, the "Responsiveness Pair Clustering Analysis" is the analysis
method using the responsiveness [7-9]of the data as the similarity measure of clusterin The
of
clustering.
cluster that purchasing behaviour is the same is created after the result of this analysis. Then
analysis
similar area (cities, towns and villages) will be visualization by mapping these clusters on the
map. This visualization system is expected to contribute as an analytical tool for customers’
contribute
purchasing behaviour and so on.
2. RELATED WORK AND BASIC TECHNOLOGY OF BIG DATA ANALYSIS
2.1. Bibliometrics
Bibliometrics is the analysis method of citation relationship proposed by Garfield [10-13].It
analyse citation relation as a target of Journal papers. There are three techniquesin the citation
papers
relation analysis as shown in - following.
③①
Direct Citation
As shown inFig1,Papers A and B are cited in Paper C, In this case, direct citation deems
①
①
①
①
that there are links between Papers A/B and Paper C and further links between Paper C.
As a result, there are three nodes and two links in the network. When direct citation is used,
a certain paper is deemed to have links with all papers that cite the pertinent paper.
Figure 1. Direct Citation
Co-Citation
This was proposed by Small[14].As shown inFig2, both Paper A and Paper B are cited in
Small
Paper C. In this case, co-citation deems that there is a link between Paper A and Paper B;
-citation
thus, there are two nodes and one link in the network. For pairs of papers in which co
cocitation was used, i.e., all papers contained in the list of cited literature of a certain paper,
there is a link between the paired papers.
Figure 2. Co-Citation
2
②
②
②
②
3. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
Bibliographic coupling
It is a technique proposed by Kessler [15]. As shown in Fig3, both Paper D and Paper E
cited Paper C. In this case, this technique deems that there is a link between Paper D and
Paper E; thus, there are two nodes and one link in the network. When bibliographic
coupling is used for pairs of papers that cite a certain paper, it is deemed that there is a link
pling
between the paired papers.
Figure 3. Bibliographic coupling
2.2. Clustering method based on the ModularityQ
Clustering method is based on the graph theory.Clustering is a technique used to divide a large
volume of data like academic papers. According to common features by clustering can simplify
ccording
the overall structure of complex data and understand it more directly and thoroughly. There are a
directly
few techniques at Clustering Methods as shown in A) -C)below.
s
A) Newman Method [16-18
8]
This technique is the clustering technique proposed by M. E. J. Newman.This technique
This
does the clustering by optimizing the Modularity Q.
B) GN Method [19]
This technique proposed by M.E.J.Newman and M.Girvan. This technique does the
clustering by using betweenness of the edge.
edge
C) CNM Method [20]
This technique proposed by Aaron Clauset, M.E.J.Newman and Cristopher Moore This
Moore.
technique is the faster technique than Newman method.
hnique
Fig4 is the citation map by Newman Method.Fig4 is the example that did divide a large volume
Fig4
of data like academic papers about "Data Mining". In this way, will be able to simplify the overall
structure of complex data and understand it more directly and thoroughly by using clustering
f
method.
3
4. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
Figure 4. E
Example of the clustering (using Newman Method)
2.3. The Problem of the Modularity Q in the Bipartite Graph
roblem
The modularity Qcan handle the network there is no restrictionon the link as shown in Table1.
can
on
For example, "Akira, O.2000" has appeared in two both the column at the Table1.This means that
Table1 This
there is no restrictionon the link.
on
link.Butmodularity Qcan’t handle the network (Bipartite Graph) there
te
is restrictionon the linklike as shown in Table2.
like
Table2.For example, there is no the data that appear in
both two columns in the Table 2.
.This means that there is restrictionon the link.
Table 1. There is no restriction on the link of network
Paper Name
Cited PapersName
Akira, O.
O.2000
Author, A2013
Akira, O.
O.2000
Author, B2011
Akira, O.
O.2000
Author, C2012
Masayoshi, K.1995
K.
Author, D2012
Masayoshi, K.1995
K.
Akira, O.2000
Masayoshi, K.1995
K.
Author, E2012
Author, E2012
E
Author, F2012
Table 2 There is restriction on the link of network
2.
Purchases Item
Purchases Place
Electrical appliances
lectrical
Electrical appliance store
Electrical appliances
lectrical
Electrical appliance store
Dress
Department store
Accessories
Department store
Cake
Supermarket
4
5. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
2.4. Related Work
There are many studies that apply modularity Qto bipartite graph. QBis the bipartite modularity
proposed by Barber[21].QB is shown as formula (1):
(1)
Pijshowsthe probability there are the link to the Vi and Vjon therandom bipartite graph. Pijis
shown as formula (2):
(2)
Aijshows the element of adjacency matrix.Aijis shown as formula (3):
(3)
Barber proposed the method of community divide at the maximum Q.
Takeshi, M.[22] did proposed bipartite Modularity QMby giving the correspondence relation to
the difference community sets.
(4)
QMis evaluatethe link densitythe most corresponds community.But it’s mean that QMcan
only evaluate about the one community.
Kazunari, I.[23] proposed the bipartite modularity
in order to address this issue.
is
shown as formula (5).
(5)
is a partitioning algorithm known as the Weakest Pair (WP) algorithm. This separates the
weakest pairs of bloggers and webpages, respectively, using co-citation information.
Finally, Keiu, H. [24] proposed a new bipartite modularity QH which is a measure to evaluate
community structure considering correspondence of the community relation quantitatively.
(6)
5
6. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
He showedC=CA CBas a bipartite graph community when each part communities are CA and
CB.(eij-aiaj)compute the difference between the expected value of link density and the density of
links between communities, like Modularity Q. But QHcan evaluate the relationship between
communities, but can’t evaluate relationship between nodes in the communities.
3. PROPOSED METHOD OF THIS STUDY
This study will propose the analysis method of responsiveness pair clustering tosocial network
bipartite graph.Then, will do implementation the social network bipartite graph visualization
system as a target to twitter data.This system is expected as a marketing system about customer
purchase behaviour.
3.1.Acquisition of the Target Data
We used our own script, to which the twitter API is applied, to obtain information from tweeted
comments about what commercial items consumers purchased during the Christmas period and
where, in order to use it as analysis data. Specifically, we acquired tweets and position
(latitude/longitude) information on from December 22 to 25, 2012. About 750 pieces of
information were obtained. Fig5 shows an example of formatted information.
(1) Tue, 22 Dec 2012,
(2) twitter_User_ID,
(3) I bought the clothes by **department store.(@ **Department storew/7 others),
(4) [35.628227, 139.738712]
Figure 5. Example of obtained information’s (tweeted comments and position information)
3.2.Morphological Analysisand Dependency Parsing
We'll explain analysis method about obtained information using morphological analysis and
dependency parsing in this section.
First, Geocoding was applied to extract the names of cities, towns and villages (position data)
from the latitude and longitude shown in (4) of Fig5. We then extracted information about what
items were purchased by consumers and where from their tweets {(3) of fig5}. As for the
shopping location, the part “@ so-and-so department” in (3) was automatically extracted. A
morphological analysis was performed on the information of (3) using ChaSen [25], and then a
rule-based Dependency Parsing [26] was done to extract information of purchased items. As a
result, a"clothes" was extracted in the case of Fig5. We manually extracted these information if
had not been extracted automatically. Dependency parsing is the method of analyse relation
words about each words as shown in Fig6.
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7. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
Figure 6. Dependency parsing
We have constructed the rule-based for extracting the subject from the verb,and then it applied to
based
and
dependency parsing.The analysis target data for "Responsiveness Pair Clustering analysis (next
The
section)" are created after the above analysis (Geocoding, morphological analysis and rule
morphological
rule-based
dependency parsing) as a bipartite graph as shown in the table3.The left column of table3 shows
table3.
the purchase informationand the right column shows cities, towns and villages (position
and
information).We will propose the method of "Responsiveness Pair Clustering analysis using the
We
Responsiveness
analysis"
bipartite graph data (table3) in the next section.
section
Table 3. The analysis target data for "Responsiveness Pair Clustering analysis (next section)"
he
The information of Purchase_Purchase place
nformation Purchase
City
Hair dryer_Home electronics retailer
ome
Shinjuku-ku, Tokyo,Japan
Refrigerator_ Home electronics retailer
ome
Asaka-City, Saitama, Japan
City,
Clothing _ Department store
Osaka-shi, Osaka, Japan
Cake _ Department store
Ichikawa-city,Chiba, Japan
,
Cake _ Supermarket
Oita-city, Oita, Japan
Cake _ Department store
Adachi-ku, Tokyo, Japan
ku,
Desk _ Supermarket
Akita-city, Akita, Japan
3.3.Responsiveness Pair Clustering analysis
Responsiveness
It is difficult to using bipartite graph at the Modularity Qas shown in section 2.3.Therefore, this
Therefore,
study conduct the hierarchical clustering using responsiveness (the similarity measure between 2
measure)
parts of the dataset (table3), in place of the modularity.
,
First of all, we consider the responsiveness to be used as the similarity measure for clustering.
Concretely, we set the similarity measure using MCA (Multiple Correspondence Analysis) by
reference to the method used by Vanables, W.N.[27], which is generally used in the statistical
[27],
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8. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
software "R". Then, the binary cross tabulation of (m n) should be set as an initial matrix for
MCA,which can be expressed as the below formula.
×
(7)
I and J represent the set of alternatives for the items of each row and column as expressed below.
I={1,2,…,m},
J={1,2,…,n}
(8)
The concept for profile is assumed as the patterns of relative ratio of the rows or columns of the
cross tabulations as indicated as shown in the below (9) and (10).
(The profile of row)
(The profile of column)
(9)
(10)
Secondly, we considering about MCA. If the variables are dichotomized, the binary cross
tabulation should be set as an initial matrix, then that matrix should be made firstly toapplyxij,
then the below (11) and (12) as its elements.
(11)
(12)
Subsequently, the elements of xij can be expressed as below, and it is the basic matrix for MCA.
(13)
Thirdly, we think about the component score of responsiveness pair analysis.Formula (14) and
(15) are the component scores of purchase information and regional information.The component
score of the k-th for the purchase node iis as the below formula.
(14)
The component score of the k-th for the regional node j is as the below formula.
(15)
Then, The relationship of probability matrix and the component scores of I and J are shown in
table4.
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9. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
Table 4. The relationship of probability matrix and the component scores of I and J
J
1
2
…
J
…
n
Row
sum
1
f11
f12
…
f1j
…
f1n
f1+
2
f21
f22
…
f2j
…
f2n
f2+
fin
…
…
f+2
…
…
…
…
fm2
…
fij
…
…
fmj
…
fmn
fm+
f+j
…
f+n
f++
…
f+1
…
Column
sum
…
fm1
fi2
…
m
…
fi1
…
I
…
…
…
I
fi+
The component scores (zik, zjk)calculated as above (14) and (15) are used as the coordinates of
matrix for hierarchical clustering.
Next, we will consider the hierarchical clustering. Generally in case of 2 variables, the
hierarchical clustering generates the clusters by merging those whose Euclidian distances are
shorter by calculating it between i and j.Then, 2 clusters that indicated the shortest distances
between each other are sequentially merged,so that it can obtain the hierarchical structure by
repeated integration of all subjects into the final one cluster. In case of 2 variables, the Euclidian
distance between the subjects i and j can be expressed as below,if the coordinate between i and j
is set as (xi1,xj1).
(16)
Also for multivariate cases, it should be defined as below by extending the formula (16).
(17)
By applying above zik and zjk to the multivariate coordinates of the formula (17),we can calculate
the Euclidian distance by using the correspondence relation as the similarity measure. This should
be expressed as the next formula.
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10. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
(18)
Next, there are a many methods of measuring the inter-cluster as shown in the following.
Nearest neighbor method
(19)
Furthest neighbor method
(20)
Group average method
(21)
Ward method
(22)
By means other than Ward’s method, there are the cases to obtain the reduced distances after
merging clusters by median point.That is to say, it cannot ensure the monotonicity of distance.
Therefore, we will use Ward method with measuring the inter-cluster.
3.4.Visualization System of Purchase and Position Information
We were implementation the visualization system based on the above methods (section3.3) as
shown in the Fig7."PurchaseInformation Network Map of Shinjuku-City" in the Fig7 is the
example of PurchaseInformation Network Map. The colour classification of the nodes expresses
difference of purchase and position information. This system canvisualizethe relationship to
other Cities, Towns and Villages by this colour classification.
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11. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
If selected the Tokyo
Japan Map
If selected the Shinjuku-City
Cities, Towns and Villages
PurchaseInformation
Network Map of ShinjukuCity
Figure 7. Visualization System of Purchase and Position Information
4. EVALUATION EXPERIMENT
4.1.Outline of Evaluation Experiment
We will compare the Harada's method (QH, Section2.4) and our method using actual SNS
data(about 700) in this evaluation experiment.We have set the correct community divided (Rn) as
an index of this evaluation experiment.The n of Rnshows the node, and we had compared the QH
and our method while increasing increments of 100 nodes.Rnis defined like follows (1. – 4.).
1.
2.
3.
4.
If "The information of Purchase _ Purchase place" is the same, these nodes will set at the
same community (table3).
If "The information of Purchase" is the same, these nodes will set at the same community.
It is to be done for the rest nodes ofabove 1.
If "Purchase place" is the same, these nodes will set at the same community. It is to be
done for the rest nodes of above 2.
Finally, if it does not match with everything node, it will be treated as a single
community.
By increasing the number of nodes in the 1 to 4 work by the hundred, changes in cluster numbers
are estimated, an estimate called Rn.
Fig8 shows the result of Rn, QH and our method. As found in Fig8, the proposed method shows a
similar pattern to Rn. In contrast, until the number of nodes reaches 400, the number of
community for QH is far larger than that for Rn.The QHcommunity number, however, is much
smaller than that of Rn after the number of nodes exceeds 400.
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250
200
N
u
m 150
b
e
r
100
c
l
u
s
t
e
r o
f
Rn
QH
Proposal Method
50
0
100
200
300
400
500
600
700
Number of Node
Figure 8. Comparison of community number ofRn, QH and Proposal Method
of
Then, A t-test is conducted to determine how much difference there is between the proposed
test
approach and QH in terms of the community number mean value. More specifically, the t
community-number
t-test is
on the difference between the mean values of the two data sets when the number of nodes in Fig8
number
is between 100 and 700. If the null hypothesis is that there is no difference between the mean
values of the proposed approach and QH, the t-test result is: 0.046<0.05, the level of significance.
test
It has thus been found that there is a statistically significant difference between the two.
statistically
4.2.Discussion of Evaluation Experiment
This section will discuss the above evaluation experiment. If communities are created based on
social networking purchase data, as shown in the Rn definition, the number of communities are
very likely to increase according to the number of nodes.The reason for this is because the
nodes.The
corresponding number itself decreasing is not possible, since the corresponding number among
nodes would accumulate proportionally to the increase of nodes.
rtionally
The QH community number, however, is much smaller than that of Rnafter the number of nodes
exceeds 400.The cause of this is considered as follows.QHis realized in the form of expanding
he
follows.
modularityQ.Modularity Q is something that carries out division in a condition where the
connections within the same community are at the most dense yet the connection to other
he connections
communities are at the least.In other words, since modularity has the characteristics of becoming
In
more coherent as the number of similar references increases, this result in the division being
carried out with smaller number of communities.This trait appears to have become prominent in
this experiment after the number of nodes exceeded 400.
400.However, as the proposed technique
takes into account this correspondence while carrying out clustering, the result has shown
transition of community number similar to that of Rn.
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13. Advanced Computing: An International Journal (ACIJ), Vol.4, No.6, November 2013
4. CONCLUSION
In this study, Collecting regional information and purchase information from Twitter and
representing them as bipartite graph, a technique to analyse "Responsiveness Pair Clustering" has
been proposed. The modularity Q can’t handle the network if there is restriction on the link. This
study was solved this problem by using the "Responsiveness Pair Clustering"instead of the
Modularity Q.Then we confirmed predominance of our method than QH by result of evaluation
experiment. Furthermore, we constructed the visualization system of customer purchase based on
this method.This visualization system is expected to contribute as an analytical system for
customers’ purchasing behaviour and so on.
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Authors
Akira Otsuki
Received his Ph.D. in engineering from Keio University (Japan), in 2012. He is
currently associate professor at Tokyo institute of technology (Japan) and Officer at
Japan society of Information and knowledge (JSIK). His research interests include
Analysis of Big Data, Data Mining, Academic Landscape, and new knowledge
creation support system. Received his Best paper award 2012 at JSIK. And received
his award in Editage Inspired Researcher Grant, in 2012.
MasayoshiKawamura
Masayoshi Kawamura is a system engineer (Japan). He received M.S. degree from
Kyoto Institute of Technology (Japan) in 1998. His research interests include image
processing, digital signal processing, and statistical data analysis.
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