The traditional medical analysis is based on the static data, the medical data is about to be analysis after
the collection of these data sets is completed, but this is far from satisfying the actual demand. Large
amounts of medical data are generated in real time, so that real-time analysis can yield more value. This
paper introduces the design of the Sentinel which can realize the real-time analysis system based on the
clustering algorithm. Sentinel can realize clustering analysis of real-time data based on the clustering
algorithm and issue an early alert.
APPLICATION OF DYNAMIC CLUSTERING ALGORITHM IN MEDICAL SURVEILLANCE cscpconf
The traditional medical analysis is based on the static data, the medical data is about to be analysis after the collection of these data sets is completed, but this is far from satisfying the actual demand. Large amounts of medical data are generated in real time, so that real-time analysis can yield more value. This paper introduces the design of the Sentinel which can realize the real-time analysis system based on the clustering algorithm. Sentinel can realize clustering analysis of real-time data based on the clustering algorithm and issue an early alert.
A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICSijistjournal
The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized processing in case of structured and semi-structured data. The under taken work is confined to analysis of both verities of methods. The result of this study is targeted to introduce methods available to analyze big data.
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Bas...acijjournal
Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a
significant opportunity for improving the quality of health care services. Physiological signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria are to solve the real-time data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting
concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in Wireless Body Area Network( WBAN) based health care environment.
An Improved Differential Evolution Algorithm for Data Stream ClusteringIJECEIAES
A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
Data performance characterization of frequent pattern mining algorithmsIJDKP
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely
huge amount of data, it is quite natural that the demand for the computational environment to accelerates,
and scales out big data applications increases. The important thing is, however, the behavior of big data
applications is not clearly defined yet. Among big data applications, this paper specifically focuses on stream mining applications. The behavior of stream mining applications varies according to the characteristics of the input data. The parameters for data characterization are, however, not clearly defined yet, and there is no study investigating explicit relationships between the input data, and streammining applications, either. Therefore, this paper picks up frequent pattern mining as one of the
representative stream mining applications, and interprets the relationships between the characteristics of the input data, and behaviors of signature algorithms for frequent pattern mining.
Data characterization towards modeling frequent pattern mining algorithmscsandit
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle
extremely huge amount of data, it is quite natural that the demand for the computational
environment to accelerates, and scales out big data applications increases. The important thing
is, however, the behavior of big data applications is not clearly defined yet. Among big data
applications, this paper specifically focuses on stream mining applications. The behavior of
stream mining applications varies according to the characteristics of the input data. The
parameters for data characterization are, however, not clearly defined yet, and there is no study
investigating explicit relationships between the input data, and stream mining applications,
either. Therefore, this paper picks up frequent pattern mining as one of the representative
stream mining applications, and interprets the relationships between the characteristics of the
input data, and behaviors of signature algorithms for frequent pattern mining.
APPLICATION OF DYNAMIC CLUSTERING ALGORITHM IN MEDICAL SURVEILLANCE cscpconf
The traditional medical analysis is based on the static data, the medical data is about to be analysis after the collection of these data sets is completed, but this is far from satisfying the actual demand. Large amounts of medical data are generated in real time, so that real-time analysis can yield more value. This paper introduces the design of the Sentinel which can realize the real-time analysis system based on the clustering algorithm. Sentinel can realize clustering analysis of real-time data based on the clustering algorithm and issue an early alert.
A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICSijistjournal
The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized processing in case of structured and semi-structured data. The under taken work is confined to analysis of both verities of methods. The result of this study is targeted to introduce methods available to analyze big data.
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Bas...acijjournal
Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a
significant opportunity for improving the quality of health care services. Physiological signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria are to solve the real-time data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting
concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in Wireless Body Area Network( WBAN) based health care environment.
An Improved Differential Evolution Algorithm for Data Stream ClusteringIJECEIAES
A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
Data performance characterization of frequent pattern mining algorithmsIJDKP
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle extremely
huge amount of data, it is quite natural that the demand for the computational environment to accelerates,
and scales out big data applications increases. The important thing is, however, the behavior of big data
applications is not clearly defined yet. Among big data applications, this paper specifically focuses on stream mining applications. The behavior of stream mining applications varies according to the characteristics of the input data. The parameters for data characterization are, however, not clearly defined yet, and there is no study investigating explicit relationships between the input data, and streammining applications, either. Therefore, this paper picks up frequent pattern mining as one of the
representative stream mining applications, and interprets the relationships between the characteristics of the input data, and behaviors of signature algorithms for frequent pattern mining.
Data characterization towards modeling frequent pattern mining algorithmscsandit
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle
extremely huge amount of data, it is quite natural that the demand for the computational
environment to accelerates, and scales out big data applications increases. The important thing
is, however, the behavior of big data applications is not clearly defined yet. Among big data
applications, this paper specifically focuses on stream mining applications. The behavior of
stream mining applications varies according to the characteristics of the input data. The
parameters for data characterization are, however, not clearly defined yet, and there is no study
investigating explicit relationships between the input data, and stream mining applications,
either. Therefore, this paper picks up frequent pattern mining as one of the representative
stream mining applications, and interprets the relationships between the characteristics of the
input data, and behaviors of signature algorithms for frequent pattern mining.
Concept Drift Identification using Classifier Ensemble Approach IJECEIAES
Abstract:-In Internetworking system, the huge amount of data is scattered, generated and processed over the network. The data mining techniques are used to discover the unknown pattern from the underlying data. A traditional classification model is used to classify the data based on past labelled data. However in many current applications, data is increasing in size with fluctuating patterns. Due to this new feature may arrive in the data. It is present in many applications like sensornetwork, banking and telecommunication systems, financial domain, Electricity usage and prices based on its demand and supplyetc .Thus change in data distribution reduces the accuracy of classifying the data. It may discover some patterns as frequent while other patterns tend to disappear and wrongly classify. To mine such data distribution, traditionalclassification techniques may not be suitable as the distribution generating the items can change over time so data from the past may become irrelevant or even false for the current prediction. For handlingsuch varying pattern of data, concept drift mining approach is used to improve the accuracy of classification techniques. In this paper we have proposed ensemble approach for improving the accuracy of classifier. The ensemble classifier is applied on 3 different data sets. We investigated different features for the different chunk of data which is further given to ensemble classifier. We observed the proposed approach improves the accuracy of classifier for different chunks of data.
Data repository for sensor network a data mining approachijdms
The development of sensor data repositories will aid the researchers to create benchmark dataset. These
benchmark dataset will provide a platform for all the researchers to access the data, test and compare the
accuracy of their algorithms. However, the storage and management of sensor data itself is a challenging
task due to various reasons such as noisy, redundant, missing, and faulty data. Therefore it is very
important to create a data repository which contains the precise and accurate data and also storage and
management of data is effective. Hence, in this paper we are proposing to use the combination of
quantitative association rules and decision tree for classification of faulty data and normal data. Usage of
multiple linear regression models for the estimation of missing data. A symbolic table approach for storage
and management of sensor data. And development of a graphical user interface for visualization of sensor
data.
Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
A Novel Approach for Clustering Big Data based on MapReduce IJECEIAES
Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.
Survey of streaming data warehouse update schedulingeSAT Journals
In this paper, we study scheduling problem of updates for the streaming data warehouses. The streaming data warehouses are the combination of traditional data warehouses and data stream systems. In this, jobs are nothing but the processes which are responsible for loading new data in the tables. Its purpose is to decrease the data staleness. In addition, it handles well, the challenges faced by the streaming warehouses like, data consistency, view hierarchies, heterogeneity found in update jobs because of dissimilar arrival times as well as size of data, preempt updates etc. The staleness of data is the scheduling metric considered here. In this, jobs are nothing but the processes which are responsible for loading new data in the tables. Its purpose is to decrease the data staleness. In addition, it handles well, the challenges faced by the streaming warehouses like, data consistency, view hierarchies, heterogeneity found in update jobs because of dissimilar arrival times as well as size of data, preempt updates etc. The staleness of data is the scheduling metric considered here.
Keywords: partitioning strategy, scalable scheduling, data stream management system.
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.
Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval IJECEIAES
Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
— The healthcare industry is considered one of the
largest industry in the world. The healthcare industry is same as
the medical industries having the largest amount of health related
and medical related data. This data helps to discover useful
trends and patters that can be used in diagnosis and decision
making. Clustering techniques like K-means, D-streams,
COBWEB, EM have been used for healthcare purposes like heart
disease diagnosis, cancer detection etc. This paper focuses on the
use of K-means and D-stream algorithm in healthcare. This
algorithms were used in healthcare to determine whether a
person is fit or unfit and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, cigarette smoking. By analyzing both the
algorithm it was found that the Density-based clustering
algorithm i.e. the D-stream algorithm proves to give more
accurate results than K-means when used for cluster formation of
historical biomedical data. D-stream algorithm overcomes
drawbacks of K-means algorithm
A time efficient and accurate retrieval of range aggregate queries using fuzz...IJECEIAES
Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of stratic variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the stratic variable might leads to the less efficient partitioning of tables. This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of stratic variable. This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy.
Your Path to YouTube Stardom Starts HereSocioCosmos
Skyrocket your YouTube presence with Sociocosmos' proven methods. Gain real engagement and build a loyal audience. Join us now.
https://www.sociocosmos.com/product-category/youtube/
“To be integrated is to feel secure, to feel connected.” The views and experi...AJHSSR Journal
ABSTRACT: Although a significant amount of literature exists on Morocco's migration policies and their
successes and failures since their implementation in 2014, there is limited research on the integration of subSaharan African children into schools. This paperis part of a Ph.D. research project that aims to fill this gap. It
reports the main findings of a study conducted with migrant children enrolled in two public schools in Rabat,
Morocco, exploring how integration is defined by the children themselves and identifying the obstacles that they
have encountered thus far. The following paper uses an inductive approach and primarily focuses on the
relationships of children with their teachers and peers as a key aspect of integration for students with a migration
background. The study has led to several crucial findings. It emphasizes the significance of speaking Colloquial
Moroccan Arabic (Darija) and being part of a community for effective integration. Moreover, it reveals that the
use of Modern Standard Arabic as the language of instruction in schools is a source of frustration for students,
indicating the need for language policy reform. The study underlines the importanceof considering the
children‟s agency when being integrated into mainstream public schools.
.
KEYWORDS: migration, education, integration, sub-Saharan African children, public school
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Abstract:-In Internetworking system, the huge amount of data is scattered, generated and processed over the network. The data mining techniques are used to discover the unknown pattern from the underlying data. A traditional classification model is used to classify the data based on past labelled data. However in many current applications, data is increasing in size with fluctuating patterns. Due to this new feature may arrive in the data. It is present in many applications like sensornetwork, banking and telecommunication systems, financial domain, Electricity usage and prices based on its demand and supplyetc .Thus change in data distribution reduces the accuracy of classifying the data. It may discover some patterns as frequent while other patterns tend to disappear and wrongly classify. To mine such data distribution, traditionalclassification techniques may not be suitable as the distribution generating the items can change over time so data from the past may become irrelevant or even false for the current prediction. For handlingsuch varying pattern of data, concept drift mining approach is used to improve the accuracy of classification techniques. In this paper we have proposed ensemble approach for improving the accuracy of classifier. The ensemble classifier is applied on 3 different data sets. We investigated different features for the different chunk of data which is further given to ensemble classifier. We observed the proposed approach improves the accuracy of classifier for different chunks of data.
Data repository for sensor network a data mining approachijdms
The development of sensor data repositories will aid the researchers to create benchmark dataset. These
benchmark dataset will provide a platform for all the researchers to access the data, test and compare the
accuracy of their algorithms. However, the storage and management of sensor data itself is a challenging
task due to various reasons such as noisy, redundant, missing, and faulty data. Therefore it is very
important to create a data repository which contains the precise and accurate data and also storage and
management of data is effective. Hence, in this paper we are proposing to use the combination of
quantitative association rules and decision tree for classification of faulty data and normal data. Usage of
multiple linear regression models for the estimation of missing data. A symbolic table approach for storage
and management of sensor data. And development of a graphical user interface for visualization of sensor
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Analysis on different Data mining Techniques and algorithms used in IOTIJERA Editor
In this paper, we discusses about five functionalities of data mining in IOT that affects the performance and that
are: Data anomaly detection, Data clustering, Data classification, feature selection, time series prediction. Some
important algorithm has also been reviewed here of each functionalities that show advantages and limitations as
well as some new algorithm that are in research direction. Here we had represent knowledge view of data
mining in IOT.
Different Classification Technique for Data mining in Insurance Industry usin...IOSRjournaljce
this paper addresses the issues and techniques for Property/Casualty actuaries applying data mining methods. Data mining means the effective unknown pattern discovery from a large amount database. It is an interactive knowledge discovery procedure which is includes data acquisition, data integration, data exploration, model building, and model validation. The paper provides an overview of the data discovery method and introduces some important data mining method for application to insurance concluding cluster discovery approaches.
A Novel Approach for Clustering Big Data based on MapReduce IJECEIAES
Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.
Survey of streaming data warehouse update schedulingeSAT Journals
In this paper, we study scheduling problem of updates for the streaming data warehouses. The streaming data warehouses are the combination of traditional data warehouses and data stream systems. In this, jobs are nothing but the processes which are responsible for loading new data in the tables. Its purpose is to decrease the data staleness. In addition, it handles well, the challenges faced by the streaming warehouses like, data consistency, view hierarchies, heterogeneity found in update jobs because of dissimilar arrival times as well as size of data, preempt updates etc. The staleness of data is the scheduling metric considered here. In this, jobs are nothing but the processes which are responsible for loading new data in the tables. Its purpose is to decrease the data staleness. In addition, it handles well, the challenges faced by the streaming warehouses like, data consistency, view hierarchies, heterogeneity found in update jobs because of dissimilar arrival times as well as size of data, preempt updates etc. The staleness of data is the scheduling metric considered here.
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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.
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Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
— The healthcare industry is considered one of the
largest industry in the world. The healthcare industry is same as
the medical industries having the largest amount of health related
and medical related data. This data helps to discover useful
trends and patters that can be used in diagnosis and decision
making. Clustering techniques like K-means, D-streams,
COBWEB, EM have been used for healthcare purposes like heart
disease diagnosis, cancer detection etc. This paper focuses on the
use of K-means and D-stream algorithm in healthcare. This
algorithms were used in healthcare to determine whether a
person is fit or unfit and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, cigarette smoking. By analyzing both the
algorithm it was found that the Density-based clustering
algorithm i.e. the D-stream algorithm proves to give more
accurate results than K-means when used for cluster formation of
historical biomedical data. D-stream algorithm overcomes
drawbacks of K-means algorithm
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Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of stratic variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the stratic variable might leads to the less efficient partitioning of tables. This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of stratic variable. This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy.
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https://www.sociocosmos.com/product-category/youtube/
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APPLICATION OF DYNAMIC CLUSTERING ALGORITHM IN MEDICAL SURVEILLANCE
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 8, No. 1, February 2018
DOI: 10.5121/ijcsea.2018.8101 1
APPLICATION OF DYNAMIC CLUSTERING ALGORITHM
IN MEDICAL SURVEILLANCE
Zhuohui Ren1
and Cong Wang2
1
Department of Software Engineering, Beijing University of Posts and Telecommunications
, BeiJing City, China
2
Department of Software Engineering, Beijing University of Posts and Telecommunications
, BeiJing City, China
ABSTRACT
The traditional medical analysis is based on the static data, the medical data is about to be analysis after
the collection of these data sets is completed, but this is far from satisfying the actual demand. Large
amounts of medical data are generated in real time, so that real-time analysis can yield more value. This
paper introduces the design of the Sentinel which can realize the real-time analysis system based on the
clustering algorithm. Sentinel can realize clustering analysis of real-time data based on the clustering
algorithm and issue an early alert.
KEYWORDS
Algorithms, Data Mining , Cluster, Data stream, Medical
1. INTRODUCTION
With the arrival of big data era, Medical big data has gradually entered the people's
vision,Medical big data refers to all the big data related to medical and life health.According to
the source of medical big data can be broadly divided into biological big data, clinical big data
and health big data[1].This The potential value of medical data is enormous.For example, public
health departments can conduct comprehensive disease surveillance in the monitoring of
infectious diseases through a nationwide electronic medical record database, and analysis the
characteristics of the spread of illness through data mining.
In the field of health care, most of the data can be seen as streaming data, such as out-patient
records, electronic medical records and so on.These data increase by the of time and the numbers
of people,It has the characteristic of continuity. Because of its real-time nature, it plays an
important role in disease surveillance.For example, mining of outpatient records can dynamically
detect diseases that increase in a large amount over a certain period of time, for example, sudden
infectious diseases or collective poisoning.Unlike traditional databases that contain static data,
data stream are inherently continuous and unconstrained, and there are many problems when
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 8, No. 1, February 2018
2
working with such data.In addition, the result of data analyse is very unstable and constantly
generating new patterns.Static pattern mining techniques proved inefficient when working with
data streams.With the deepening of information technology in the medical field, the ability of
generating data is rapidly increasing. Mining useful information from these streams has become
an inevitable task.
2. RELATED WORK
Sudipto Guha proposed a clustering algorithm based on stream data [2].In his algorithm, the idea
of divide and conquer is adopted, the data flow is divided into multiple segments, and the
segments is separately clustered to obtain the first cluster center.When the first cluster center
reaches a certain number, the second segment of data is introduced to cluster to get the second
cluster center. As data continues to flow in, this process will continue. At each time point, the
system only needs to maintain m i-th layer center points.This division of mind is very efficient for
the analysis of streaming data.Since only a limited number of data needs to be saved at each time
point in the system, the storage and memory shortage due to the large incoming stream data are
avoided.Because the stream data analysis is a dynamic process, Most of the algorithms are based
on the needs of the application to choose the time as a standard, select a period of time to
analysis. According to the selected timing range can be divided into snapshot model, landmark
model And sliding window model. Landmark model and sliding window model are more used.
As an important algorithm in data mining, the main goal of clustering is to classify the internal
relations between data into a large category and distinguish each category as much as possible,
which is an extension in taxonomy.According to the different basic principles of clustering can be
divided into,division clustering,hierarchical clustering,density-based clustering, model-based
clustering and grid-based clustering [3].
With the extension time or space will produce a wide range of data, and data mining is to extract
valuable information from these complex types of data. These complex types of data can be
divided into spatial data, timing data, web data, text data [4]. From its process, dynamic data
mining can be divided into several stages such as dynamic data collection, data processing, data
mining, and mining evaluation [5]. In general, data mining and mining evaluation are closely
integrated. Dynamic data mining needs better handling of real-time data and the impact of real-
time data on analysis results. The main problems of k-mean algorithm in dealing with dynamic
data mining are as follows: Since the initial value of k is fixed means that it can not be changed
after it is selected, that makes k-means algorithm unsuitable for mining of dynamic
data.Therefore, in the k-means algorithm for dynamic data clustering algorithm, the improvement
mainly focuses on the selection and dynamic adjustment of the k value [6], which can be mainly
divided into two directions:1, in the process of dynamic data acquisition of data preprocessing,
according to the predetermined strategy to adjust the size of k;2 In the data mining process
according to the data mining results and predetermined criteria, the data results are dynamically
adjusted, and then update k value.The difference between the two methods is that the former is
adjusted in the data processing stage and the latter is adjusted in the data mining stage.
The algorithms based on the first idea are: K-means clustering algorithm based on KD tree[6].
The KD tree represents a k-dimensional storage structure that stores data separately at each node
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 8, No. 1, February 2018
3
in a well-spaced space. Since the initial cluster centers in the k-means algorithm are randomly
selected, they can not reflect the true distribution of the data. In order to distribute the actual
reaction data as much as possible, it is better to distribute the initial center points more evenly.
The basic idea of clustering using KD tree is as follows: Firstly, the advantages of KD tree are
used to divide the spatial extent of data set and the data of the corresponding interval is stored.
This will effectively improve the effect of the initial center point selection. Using KD tree to
divide the space and preprocess the data, we can know the distribution of the data truly. Then
according to the partitioned interval, the initial center point is chosen directionally. Finally, the
clustering operation is carried out. The algorithm can better find the k-value and the clustering
center point, but the computational cost is larger when the clustering operation is re-performed.
Compared with the first method of dynamically adjusting the k value, the second method is based
on The computation overhead caused by the local dynamic adjustment of the clustering result and
the result evaluation index will be significantly reduced. For dynamic data sets, it is obviously
inefficient to re-execute the clustering algorithm on the updated new data set to update the
clustering results accordingly, so it is very important to adopt incremental clustering algorithm
effectively .
Among them, the algorithm based on the second idea has a two-point k-means clustering
algorithm [7]. The main idea is to adjust the clustering result locally instead of the global
adjustment according to the threshold in the process of data clustering, which can effectively
improve the efficiency of the algorithm and does not affect the final result of the clustering.
3. PROBLEM SETUP
The main problem in the processing of streaming data is that streaming data is a sequence of data
sequences that is massively and continuously arriving[8]. When the clustering algorithm is
applied to streaming data, it is mainly necessary to consider the real-time performance and the
scale unpredictability of the streaming data.
Sentinel's main process is as following steps:
Step 1. Monitoring data cache, if the cache data to meet the conditions to step two.
Step 2. Cached data submitted to the data analysis module, analysis module is used for data
analysis, and based on the results, update corresponding parameters.
Step 3. Data early warning module to update the data to determine, greater than the
predetermined value issued a alert.
Step 4. Return to step one.
Data caching is mainly for real-time streaming data processing, according to Sudipto Guha’
algorithms in the treatment of streaming data, the idea of data segmentation processing,
application cache technology can be very good to achieve this. The process of caching is to
segment the data base on time line. Data analysis moudle is mainly based on dynamic clustering
algorithm. In the data analysis of a block need to use the relevant information in the database, the
information is the system needs long-term maintenance. The content of this information includes
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 8, No. 1, February 2018
4
the number of clusters, the center of each cluster, and the data set that belongs to each cluster.
The data processing flow train of thought is as following steps:
Step 1 According to the cluster center stored in the system, the data will be assigned to the
corresponding cluster.
Step 2 pairs of clusters of data are calculated and compared with the threshold, according to the
comparison result,then adjust of clusters.
Step 3 According to the results of the adjustment, update the relevant records in the database.
In the local adjustment of the cluster, the main reference is the intra-cluster similarity and inter-
cluster similarity. The inter-cluster similarity is defined as the mean of the data in the cluster. The
similarity between clusters is defined as the distance between the centers of two adjacent clusters.
If the similarity between clusters in a cluster is greater than the threshold, the k-means algorithm
for k = 2 is performed on the cluster[9]. If the cluster similarity between two clusters is greater
than the threshold, the two clusters are merged.
4. CONCLUSION AND FUTURE WORK
There are some places in the system design that deserve further study, mainly for setting the
threshold of division and consolidation. The setting of the threshold determines the quality of the
splitting and merging[10]. At the same time, the setting of the threshold has a great relationship
with the selection of data types. How to find out a suitable model to adapt the model to more
types of The data set will be very necessary. Relevant researchers can conduct in-depth research
based on different subjects in medical data, and build a better model to make the algorithm better
adapt to various data mining.
REFERENCES
[1] Meng Qun, Bi Dan, Zhang Yiming et al .Chinese Journal of Health Information Management, 2016,
13 (6): 547-552.
[2] Sudipto Guha. Asymmetric k-center is log^*n-hard to Approximate[J]. Journal of the Acm, 2013,
52(4):538-551.
[3] Wang Juan.Study on Evolutionary Clustering Algorithm in Dynamic Data Mining [D]. Nanjing
University of Aeronautics and Astronautics, 2012.
[4] Zhang Yufeng, Zeng Yitang, Hao Yan.Study on Intelligent Strategy of Logistics Information Based
on Dynamic Data Mining [J] .Library Science, 2016 (5): 46-49.
[5] Wang Lunwen, Feng Yanqing, Zhang Ling.A Review of Constructive Learning Methods for
Dynamic Data Mining [J] .Microsoft Microcomputer Systems, 2016, 37 (9): 1953-1958.
[6] Tang C, Ling C X, Zhou X, et al. Proceedings of the 4th international conference on Advanced Data
Mining and Applications[C]// International Conference on Advanced Data Mining and Applications.
Springer-Verlag, 2008:15-15.
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 8, No. 1, February 2018
5
[7] Lunwen Wang, Yanqing Feng, Ling Zhang.A Review of Constructive Learning Methods for
Dynamic Data Mining [J] .Microsoft Microcomputer Systems, 2016, 37 (9): 1953-1958.
[8] Xiujin Shi, Yanling Hu,et al.Privacy Protection of Dynamic Set-valued Data Publishing Based on
Classification Tree [J] .Computer Science, 2017, 44 (5): 120-124.
[9] Guangcong Liu,Tingting Huang,Haiin Chen,et al.An improved dichotomous K-means clustering
algorithm [J].Computer Applications and Software, 2015 (2): 261-263.
[10] Zhu Y T, Wang F Z, Shan X H, et al. K-medoids clustering based on MapReduce and optimal search
of medoids[C]// International Conference on Computer Science & Education. IEEE, 2014:573-577.
Authors
Ren Zhuohui, Male, 1989, renzh@bupt.edu.cn, Master's degree of Beijing University of Posts and
Telecommunications. Main research areas include privacy protection and data mining Wang Cong, Female,
1958, Professor and doctoral tutor at Beijing University of Posts and Telecommunications. Main research
areas include intelligent control and Wisdom-Web information security.