16-02-2010, 11:08 AM
i want the code and documentation of the project : C-Trend: Temporal Cluster Graphs For Identifying And Visualizing Trends In Multiattribute Transactional Data
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C-Trend: Temporal Cluster Graphs For Identifying And Visualizing Trends In Multiattri
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i want the code and documentation of the project : C-Trend: Temporal Cluster Graphs For Identifying And Visualizing Trends In Multiattribute Transactional Data
02-03-2010, 07:04 AM
C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data
Abstractâ€Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multiattribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multiattribute temporal data using a clusteringbased approach. We introduce Cluster-based Temporal Representation of EveNt Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multiattribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, we present our temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.
Presented By:
Gediminas Adomavicius, Member, IEEE, and Jesse Bockstedt
read full report
http://ieeexplore.ieeeiel5/69/4494368/04...er=4445669
http://citeseerx.ist.psu.edu/viewdoc/dow...1&type=pdf
Abstractâ€Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multiattribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multiattribute temporal data using a clusteringbased approach. We introduce Cluster-based Temporal Representation of EveNt Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multiattribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, we present our temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.
Presented By:
Gediminas Adomavicius, Member, IEEE, and Jesse Bockstedt
read full report
http://ieeexplore.ieeeiel5/69/4494368/04...er=4445669
http://citeseerx.ist.psu.edu/viewdoc/dow...1&type=pdf
27-03-2010, 12:18 AM
i would like to have the implementation of this paper..if anybody helps it will be of a great help to me...thanx
eagerly looking for ur help..please it is urgent..please
my mail id: prkvkr[at]gmail.com
eagerly looking for ur help..please it is urgent..please
my mail id: prkvkr[at]gmail.com
13-04-2010, 06:05 PM
C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi-attribute Transactional Data
Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multi-attribute temporal data using a clustering based approach. We introduce Cluster-based Temporal Representation of EveNt Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multi-attribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time.
Business intelligence applications represent an important opportunity for data mining techniques to help firms gather and analyze information about their performance, customers, competitors, and business environment. Knowledge representation and data visualization tools constitute one form of business intelligence techniques that present information to users in a manner that supports business decision-making processes. Business intelligence tools gain their strength by supporting decision-makers, and our technique helps the users leverage their domain expertise to generate knowledge visualization diagrams from complex data and further customize them.
Existing system:
Antunes and Oliveira note that two fundamental problems that must be addressed in temporal data mining are the representation of data and data preprocessing. Also,Roddick and Spiliopoulou distinguish between two tactical goals of temporal data mining: prediction of the values of a populationâ„¢s characteristics and the identification of similarities among members of a population. They categorize the temporal data mining research along three dimensions: data type, mining paradigm, and temporal ordering.
Temporal data mining approaches depend on the nature of the event sequence being studied. Probably the most common form of temporal data miningâ€time series analysis- is used to mine a sequence of continuous real-valued elements and is often regression based, relying on the prespecified definition of a model.
Much research has focused on applying data mining techniques to discover patterns in time series data. Berndt and Clifford use a dynamic programming technique to match time series with predefined templates. Keogh and Smyth use a probabilistic approach to quickly identify patterns in time series by matching known templates to the data. Povinelli and Feng use concepts from data mining and dynamical systems to develop a new framework and method for identifying patterns in time series that are significant for characterizing and predicting events. Research on clustering in time series data extends traditional clustering into the temporal data mining domain. For example, Kakizawa et al. use minimum discrimination information for the classification and clustering of multivariate time series, Oates uses clustering to identify subsequences in multivariate real-valued time series, Kandogan introduces a new visualization technique called Star Coordinates for identifying trends in clusters, and Molinari et al. introduce a new technique for determining single and multiple temporal clustering for populations with varying sizes.
Proposed system:
We present a new data mining technique for identifying and visualizing trends in multiattribute temporal data. We build on both temporal data mining techniques and visual data exploration techniques and develop a tool that provides the user with the ability to interact with temporal cluster graph data visualization. Temporal cluster graphs use hierarchical and graph-based techniques to explore temporal data and provide interactive filtering and zooming capabilities for visualization. We define a new analytical construct called the temporal cluster graph. This graph is obtained as a result of several steps. By harnessing computational techniques of data mining, we have developed a new temporal clustering technique for discovering, analyzing, and visualizing trends in multiattribute temporal data. The proposed technique is versatile, and the implementation of the technique as the C-TREND system gives significant data representation power to the userâ€domain experts have the ability to adjust parameters and clustering mechanisms to fine-tune trend graphs. We demonstrated that the C-TREND implementation is scalable: the time required to adjust trend parameters is quite low even for larger data sets, which provides for real-time visualization capabilities. Furthermore, the proposed temporal clustering analysis technique is applicable in many different data analysis contexts and can provide insights for analysts performing historical analyses and generating forecasts.
Modules:
Transformation of dataâ„¢s from excel sheet
Needed data is collected from net and stored in the excel sheet. Data is stored into Dataset through import and export data wizard present in the SQL server.
Creation Partition of dataset
Separating of data present in the dataset according to the companies name and storing the values in separate tables. In this module, we present a new data mining technique for identifying and visualizing trends in multi attribute temporal data. We build on both temporal data mining techniques and visual data exploration techniques and develop a tool that provides the user with the ability to interact with temporal cluster graph data visualization. Temporal cluster graphs use hierarchical and graph-based techniques to explore temporal data and provide interactive filtering and zooming capabilities for visualization.
Partition of dataset
Separating of data present in the dataset according to the companies name and storing the values in separate tables. In this module, we present a new data mining technique for identifying and visualizing trends in multi attribute temporal data. We build on both temporal data mining techniques and visual data exploration techniques and develop a tool that provides the user with the ability to interact with temporal cluster graph data visualization. Temporal cluster graphs use hierarchical and graph-based techniques to explore temporal data and provide interactive filtering and zooming capabilities for visualization.
Dendrogram sorting
Apply dendogram extract algorithm to sort the value present in the partition. DENDRO_EXTRACT starts at the root of the dendrogram and traverses the dendrogram by splitting the highest numbered node in the current set of clusters until k clusters are included in the set. MaxCl represents the highest element in the current cluster set CurrCl. It is easy to see that because of the specific dendrogram structure, it is always the case that MaxCl ¼ Dendrogram Rooti _ jCurrClj Furthermore, the dendrogram data array maintains the successive levels of the hierarchical solution in order; therefore, replacing MaxCl by its children MaxCl:Left (leftchild) and MaxCl:Right (right child) is sufficient for identifying the next solution level in the dendrogram. DENDRO_EXTRACT is linear in time complexity, which provides for the real-time extraction of cluster solutions.
The implementation requires following resources:
Hardware requirements:
Pentium Processor, 1GB RAM
Software requirements:
JDK 5.0, J2EE, MS SQL Server 2005
Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multidimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multi-attribute temporal data using a clustering based approach. We introduce Cluster-based Temporal Representation of EveNt Data (C-TREND), a system that implements the temporal cluster graph construct, which maps multi-attribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time.
Business intelligence applications represent an important opportunity for data mining techniques to help firms gather and analyze information about their performance, customers, competitors, and business environment. Knowledge representation and data visualization tools constitute one form of business intelligence techniques that present information to users in a manner that supports business decision-making processes. Business intelligence tools gain their strength by supporting decision-makers, and our technique helps the users leverage their domain expertise to generate knowledge visualization diagrams from complex data and further customize them.
Existing system:
Antunes and Oliveira note that two fundamental problems that must be addressed in temporal data mining are the representation of data and data preprocessing. Also,Roddick and Spiliopoulou distinguish between two tactical goals of temporal data mining: prediction of the values of a populationâ„¢s characteristics and the identification of similarities among members of a population. They categorize the temporal data mining research along three dimensions: data type, mining paradigm, and temporal ordering.
Temporal data mining approaches depend on the nature of the event sequence being studied. Probably the most common form of temporal data miningâ€time series analysis- is used to mine a sequence of continuous real-valued elements and is often regression based, relying on the prespecified definition of a model.
Much research has focused on applying data mining techniques to discover patterns in time series data. Berndt and Clifford use a dynamic programming technique to match time series with predefined templates. Keogh and Smyth use a probabilistic approach to quickly identify patterns in time series by matching known templates to the data. Povinelli and Feng use concepts from data mining and dynamical systems to develop a new framework and method for identifying patterns in time series that are significant for characterizing and predicting events. Research on clustering in time series data extends traditional clustering into the temporal data mining domain. For example, Kakizawa et al. use minimum discrimination information for the classification and clustering of multivariate time series, Oates uses clustering to identify subsequences in multivariate real-valued time series, Kandogan introduces a new visualization technique called Star Coordinates for identifying trends in clusters, and Molinari et al. introduce a new technique for determining single and multiple temporal clustering for populations with varying sizes.
Proposed system:
We present a new data mining technique for identifying and visualizing trends in multiattribute temporal data. We build on both temporal data mining techniques and visual data exploration techniques and develop a tool that provides the user with the ability to interact with temporal cluster graph data visualization. Temporal cluster graphs use hierarchical and graph-based techniques to explore temporal data and provide interactive filtering and zooming capabilities for visualization. We define a new analytical construct called the temporal cluster graph. This graph is obtained as a result of several steps. By harnessing computational techniques of data mining, we have developed a new temporal clustering technique for discovering, analyzing, and visualizing trends in multiattribute temporal data. The proposed technique is versatile, and the implementation of the technique as the C-TREND system gives significant data representation power to the userâ€domain experts have the ability to adjust parameters and clustering mechanisms to fine-tune trend graphs. We demonstrated that the C-TREND implementation is scalable: the time required to adjust trend parameters is quite low even for larger data sets, which provides for real-time visualization capabilities. Furthermore, the proposed temporal clustering analysis technique is applicable in many different data analysis contexts and can provide insights for analysts performing historical analyses and generating forecasts.
Modules:
Transformation of dataâ„¢s from excel sheet
Needed data is collected from net and stored in the excel sheet. Data is stored into Dataset through import and export data wizard present in the SQL server.
Creation Partition of dataset
Separating of data present in the dataset according to the companies name and storing the values in separate tables. In this module, we present a new data mining technique for identifying and visualizing trends in multi attribute temporal data. We build on both temporal data mining techniques and visual data exploration techniques and develop a tool that provides the user with the ability to interact with temporal cluster graph data visualization. Temporal cluster graphs use hierarchical and graph-based techniques to explore temporal data and provide interactive filtering and zooming capabilities for visualization.
Partition of dataset
Separating of data present in the dataset according to the companies name and storing the values in separate tables. In this module, we present a new data mining technique for identifying and visualizing trends in multi attribute temporal data. We build on both temporal data mining techniques and visual data exploration techniques and develop a tool that provides the user with the ability to interact with temporal cluster graph data visualization. Temporal cluster graphs use hierarchical and graph-based techniques to explore temporal data and provide interactive filtering and zooming capabilities for visualization.
Dendrogram sorting
Apply dendogram extract algorithm to sort the value present in the partition. DENDRO_EXTRACT starts at the root of the dendrogram and traverses the dendrogram by splitting the highest numbered node in the current set of clusters until k clusters are included in the set. MaxCl represents the highest element in the current cluster set CurrCl. It is easy to see that because of the specific dendrogram structure, it is always the case that MaxCl ¼ Dendrogram Rooti _ jCurrClj Furthermore, the dendrogram data array maintains the successive levels of the hierarchical solution in order; therefore, replacing MaxCl by its children MaxCl:Left (leftchild) and MaxCl:Right (right child) is sufficient for identifying the next solution level in the dendrogram. DENDRO_EXTRACT is linear in time complexity, which provides for the real-time extraction of cluster solutions.
The implementation requires following resources:
Hardware requirements:
Pentium Processor, 1GB RAM
Software requirements:
JDK 5.0, J2EE, MS SQL Server 2005
20-12-2010, 09:30 AM
22-06-2011, 01:02 PM
i want the implementation of this paper clearly step by step
my mail id: praveendharmasai3[at]gmail.com
my mail id: praveendharmasai3[at]gmail.com
02-07-2011, 03:26 PM
please can any 1 send C-TREND ppt'ss to my ID rechercherhoney[at]gmail.com for my MTech project reviews
vina
vina
25-01-2012, 10:53 AM
to get information about the topic C-TREND:Temporal Cluster Graphs for Identifying and Visualizing Trends full report ,ppt and related topic refer the page link bellow
http://studentbank.in/report-c-trend-tem...ing-trends
http://studentbank.in/report-c-trend-tem...multiattri
http://studentbank.in/report-c-trend-tem...8#pid63598
http://studentbank.in/report-c-trend-tem...ing-trends
http://studentbank.in/report-c-trend-tem...multiattri
http://studentbank.in/report-c-trend-tem...8#pid63598
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