Papers by Priyanka Puppala
Parameter Sweeping Programming Model in Aneka on Data Mining Applications
Data mining applications and techniques are used in many areas as a required knowledge discovery ... more Data mining applications and techniques are used in many areas as a required knowledge discovery from large data sets. Cloud computing is one of the prevailing models based on IP architecture. Cloud computing is nothing but the delivery of the computing services over the internet to improve the business of many organizations. Cloud systems which can be effectively handle parallel mining since they provide scalable storage and processing services, software platforms for developing and running data mining applications. In this paper, we present a data mining application in .NET frame work that supports the execution of parameter sweeping programming model on cloud. Parameter sweeping is an important task in the domains of the system modeling and optimization. The frame work has been implemented using Aneka platform. Parameter sweeping applications can be highly computing demanding, since the number of single tasks to be executed increases with the number of swept parameters and the ra...
International Journal of Computer Science and Mobile Computing, May 29, 2014
We have been studying the problem of clustering data objects. As we have implemented a new algori... more We have been studying the problem of clustering data objects. As we have implemented a new algorithm EMaRC which is An Efficient Map Reduce algorithm for Clustering Data. In clusters Feature selection is the most important part of the clustering process that involves and identifying the set of features of a subset, at which they produces accurate and accordant results with the original set of features. The main concept behind this paper is that, to give the effective outcomes of clustering features. In this the nature of clustering and some more concepts serves for processing large data sets. A map-reduce concept is involved followed by feature selection algorithm which affects the entire process of clustering to get the most effective and features produces efficiently. While efficiency concerns, the time complexity is desirable component, which the time required to find effective features, where effectiveness is related to the quality of the features of subsets. Based on these criteria, a cluster based map-reduce feature selection approach, is proposed and evaluated in this paper.
Survey on Secure Data mining in Cloud Computing
Data mining techniques are very important in the cloud computing paradigm. The integration of dat... more Data mining techniques are very important in the cloud computing paradigm. The integration of data mining techniques with Cloud computing allows the users to extract useful information from a data warehouse that reduces the costs of infrastructure and storage. Security and privacy of user’s data is a big concern when data mining is used with cloud computing. Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services of the internet. An important security concern is privacy attacks based on data mining involving analyzing data over a long period to extract valuable information. In this dissertation our main objective is to provide information with the help of which we can make data secure from unauthorized users. As uses of data come on front we have to face concept of data mining. Data Mining is a field where accuracy matters a lot. Data mining techniques and applications are very much needed in the cloud computing p...
Feature subset clustering is a powerful technique to reduce the dimensionality of feature vectors... more Feature subset clustering is a powerful technique to reduce the dimensionality of feature vectors for text classification and involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A novel approach called supervised attribute clustering algorithm is proposed to improve the accuracy and check the probability of the patterns. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. Efficiency is related to the time required to find a subset of features while the effectiveness is related to quality of subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree clustering method.
IJCSMC, Vol. 3, Issue. 5, May 2014, pg.1013 – 1021, May 29, 2014
We have been studying the problem of clustering data objects. As we have implemented a new
algor... more We have been studying the problem of clustering data objects. As we have implemented a new
algorithm EMaRC which is An Efficient Map Reduce algorithm for Clustering Data. In clusters Feature
selection is the most important part of the clustering process that involves and identifying the set of features of a
subset, at which they produces accurate and accordant results with the original set of features. The main concept
behind this paper is that, to give the effective outcomes of clustering features. In this the nature of clustering and
some more concepts serves for processing large data sets. A map-reduce concept is involved followed by feature
selection algorithm which affects the entire process of clustering to get the most effective and features produces
efficiently. While efficiency concerns, the time complexity is desirable component, which the time required to find
effective features, where effectiveness is related to the quality of the features of subsets. Based on these criteria, a
cluster based map-reduce feature selection approach, is proposed and evaluated in this paper.
International Journal of Computer Science and Information Technologies, Apr 11, 2014
Data mining is about explaining the past and
predicting the future by means of data analysis. Ed... more Data mining is about explaining the past and
predicting the future by means of data analysis. Educational
Data Mining is a promising discipline which has an imperative
impact on predicting students’ academic performance.
Thousands of students take admissions in Universities and
colleges every year, at the time of admissions they collect the
students’ data. In the same way while the Teachers join in the
institution they collect their personal and professional data.
Understand the importance of data is essential from a
business point of view. Data collected at the time of admission
can be used for classifying and predicting students’ behavior
and performance as well as teachers’ performance.
Therefore, in this paper, we are examining the role of Data
mining in an Educational Field. By using SDAR, we have
identified possible grade values i.e., Excellent, Good, Average
and Poor or Fail. We have used K-means clustering algorithm
to find the best cluster center for attributes like attendance,
Sessional marks and assignment marks etc. We have also
discussed a Rule-Based Classification (RBC) method; it
extracts a set of rules that shows relationships between
attributes of the data set and the class label. In this paper we
have also addressed the evaluation of Teachers’ performance
by using data mining techniques at University and College
level.
International Journal of Emerging Technology and Advanced Engineering, Apr 23, 2014
Data mining applications and techniques are
used in many areas as a required knowledge discovery... more Data mining applications and techniques are
used in many areas as a required knowledge discovery from
large data sets. Cloud computing is one of the prevailing
models based on IP architecture. Cloud computing is nothing
but the delivery of the computing services over the internet to
improve the business of many organizations. Cloud systems
which can be effectively handle parallel mining since they
provide scalable storage and processing services, software
platforms for developing and running data mining
applications. In this paper, we present a data mining
application in .NET frame work that supports the execution of
parameter sweeping programming model on cloud. Parameter
sweeping is an important task in the domains of the system
modeling and optimization. The frame work has been
implemented using Aneka platform. Parameter sweeping
applications can be highly computing demanding, since the
number of single tasks to be executed increases with the
number of swept parameters and the range of their values. In
this paper the parameter sweeping model is implemented on a
data set by using the design explorer user interface.
International Journal of Advanced Research in Computer Science & Technology
Data mining techniques are very important in the cloud computing paradigm. The integration of dat... more Data mining techniques are very important in the cloud computing paradigm. The integration of data mining techniques with
Cloud computing allows the users to extract useful information from a data warehouse that reduces the costs of infrastructure and
storage. Security and privacy of user’s data is a big concern when data mining is used with cloud computing. Cloud computing is
an emerging computing paradigm in which resources of the computing infrastructure are provided as services of the internet. An
important security concern is privacy attacks based on data mining involving analyzing data over a long period to extract valuable
information. In this dissertation our main objective is to provide information with the help of which we can make data secure from
unauthorized users. As uses of data come on front we have to face concept of data mining. Data Mining is a field where accuracy
matters a lot. Data mining techniques and applications are very much needed in the cloud computing paradigm. The implementation
of data mining techniques through Cloud computing will allow the users to retrieve meaningful information from virtually integrated
data warehouse that reduces the costs of infrastructure and storage.
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Papers by Priyanka Puppala
algorithm EMaRC which is An Efficient Map Reduce algorithm for Clustering Data. In clusters Feature
selection is the most important part of the clustering process that involves and identifying the set of features of a
subset, at which they produces accurate and accordant results with the original set of features. The main concept
behind this paper is that, to give the effective outcomes of clustering features. In this the nature of clustering and
some more concepts serves for processing large data sets. A map-reduce concept is involved followed by feature
selection algorithm which affects the entire process of clustering to get the most effective and features produces
efficiently. While efficiency concerns, the time complexity is desirable component, which the time required to find
effective features, where effectiveness is related to the quality of the features of subsets. Based on these criteria, a
cluster based map-reduce feature selection approach, is proposed and evaluated in this paper.
predicting the future by means of data analysis. Educational
Data Mining is a promising discipline which has an imperative
impact on predicting students’ academic performance.
Thousands of students take admissions in Universities and
colleges every year, at the time of admissions they collect the
students’ data. In the same way while the Teachers join in the
institution they collect their personal and professional data.
Understand the importance of data is essential from a
business point of view. Data collected at the time of admission
can be used for classifying and predicting students’ behavior
and performance as well as teachers’ performance.
Therefore, in this paper, we are examining the role of Data
mining in an Educational Field. By using SDAR, we have
identified possible grade values i.e., Excellent, Good, Average
and Poor or Fail. We have used K-means clustering algorithm
to find the best cluster center for attributes like attendance,
Sessional marks and assignment marks etc. We have also
discussed a Rule-Based Classification (RBC) method; it
extracts a set of rules that shows relationships between
attributes of the data set and the class label. In this paper we
have also addressed the evaluation of Teachers’ performance
by using data mining techniques at University and College
level.
used in many areas as a required knowledge discovery from
large data sets. Cloud computing is one of the prevailing
models based on IP architecture. Cloud computing is nothing
but the delivery of the computing services over the internet to
improve the business of many organizations. Cloud systems
which can be effectively handle parallel mining since they
provide scalable storage and processing services, software
platforms for developing and running data mining
applications. In this paper, we present a data mining
application in .NET frame work that supports the execution of
parameter sweeping programming model on cloud. Parameter
sweeping is an important task in the domains of the system
modeling and optimization. The frame work has been
implemented using Aneka platform. Parameter sweeping
applications can be highly computing demanding, since the
number of single tasks to be executed increases with the
number of swept parameters and the range of their values. In
this paper the parameter sweeping model is implemented on a
data set by using the design explorer user interface.
Cloud computing allows the users to extract useful information from a data warehouse that reduces the costs of infrastructure and
storage. Security and privacy of user’s data is a big concern when data mining is used with cloud computing. Cloud computing is
an emerging computing paradigm in which resources of the computing infrastructure are provided as services of the internet. An
important security concern is privacy attacks based on data mining involving analyzing data over a long period to extract valuable
information. In this dissertation our main objective is to provide information with the help of which we can make data secure from
unauthorized users. As uses of data come on front we have to face concept of data mining. Data Mining is a field where accuracy
matters a lot. Data mining techniques and applications are very much needed in the cloud computing paradigm. The implementation
of data mining techniques through Cloud computing will allow the users to retrieve meaningful information from virtually integrated
data warehouse that reduces the costs of infrastructure and storage.