C-TREND:Temporal Cluster Graphs for Identifying and Visualizing Trends
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13-04-2010, 06:02 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 mining”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 user”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
Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
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projectsofme
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13-10-2010, 11:28 AM


.pdf   C-TREND A New Technique for Identifying Trends in Transactional Data.pdf (Size: 23.7 KB / Downloads: 86)
This article is presented by:
Gediminas Adomavicius
Jesse Bockstedt
Department of Information and Decision Sciences
Carlson School of Management, University of Minnesota


C-TREND: A New Technique for Identifying Trends in Transactional Data



Research Question and Motivation
The research field of data mining has developed sophisticated methods for identifying patterns in data in order to provide insights to users. Identifying temporal relationships (e.g., trends) in data constitutes an important problem that is relevant in many business and academic settings, and the data mining literature has provided analytical techniques for some specialized types of temporal data, e.g., time series analysis (Brockwell and Davis 2001, Keogh and Kasetty 2003, Roddick and Spiliopoulou 2002) and sequence analysis (Pei et al. 2004, Zaki 2001) techniques. Temporal data can take many forms, most commonly being general transactional (multi)attribute-value data, for which time series or sequence analysis methods are not particularly well suited. The ability to identify trends in general temporal data can provide significant benefits, such as competitive advantages to a firm performing forecasts or making decisions on future investments and strategies. In particular, the research presented in this paper is focused on answering the question: How should transactional attribute-value data be presented so that trends can be clearly identified and analyzed? Consider the problem of technology forecasting for a firm. Technologies possess many features that change over time and understanding how a technology evolves requires trend analysis of multiple attributes at once. Similar issues arise in the trend analysis of consumer purchasing behavior and many other business intelligence applications; however, current temporal analytical techniques do not provide rich visualization of such trends. In this paper, we present a new approach that uses data mining techniques to provide visualization capabilities for trend analysis in multi-attribute transactional data.
2. Approach
In this paper, we present C-TREND, Cluster-based Temporal Representation of EveNt Data, a new method for discovering and visualizing trends and temporal patterns in transactional attribute-value data that builds upon standard data mining clustering techniques. In particular, CTREND separates data into user-defined partitions based on time periods and then identifies clusters of the dominant transaction types occurring within each partition. Clusters are then compared to the clusters in adjacent time periods to identify cross-period similarities and, over many time periods, trends are identified. Trends are presented in an output graph that uses nodes to represent dominant transaction types and edges to represent cross-time relationships. The proposed C-TREND technique consists of two major processes: offline preprocessing of the data and online interactive analysis and visualization of the trends. Offline preprocessing includes the calculation of a dendrogram solution using agglomerative hierarchical clustering for each data partition and the calculation of the potential graph constructs (Duda et al. 2000). Graph nodes are determined for each partition by first extracting a k-sized clustering solution from the dendrogram and then filtering the identified clusters using the within-period trend strength parameter α. Edges are determined by calculating pair-wise similarity metrics between clusters in adjacent data partitions and filtering them using the cross-period trend strength parameter β. Interactive analysis includes the presentation of output graphs in a graphical user interface (GUI) that allows the user to adjust k for each partition and the α and β parameters, which prompts C-TREND to redraw the output graph based on these new values in real-time.
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rahuldyagam
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26-03-2011, 01:16 PM

send me c-trend project and implimentation code and documentation
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tweetyvina
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02-07-2011, 03:18 PM

hi dis is pravina , cn any 1 provide me C_TREND datamining ppt';s for my MTech project and implimentation as soon as possible
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tarandeep singh
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24-01-2012, 10:14 PM

if u got this then please forward it to me ,tanuj_arora92@yahoo.com.
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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 link bellow

topicideashow-to-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends

topicideashow-to-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends-in-multiattri

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