C-Trend: Temporal Cluster Graphs For Identifying And Visualizing Trends In Multiattri
Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
kalyan9029
Active In SP
**

Posts: 1
Joined: Feb 2010
#1
16-02-2010, 11:08 AM


i want the code and documentation of the project and implimentation : C-Trend: Temporal Cluster Graphs For Identifying And Visualizing Trends In Multiattribute Transactional Data


Attached Files
.pdf   C TREND.pdf (Size: 2.3 MB / Downloads: 352)
Reply
project report tiger
Active In SP
**

Posts: 1,062
Joined: Feb 2010
#2
02-03-2010, 07:04 AM

C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data

Abstract”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
ieeexplore.ieeeiel5/69/4494368/04445669.pdf?arnumber=4445669
citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.6127&rep=rep1&type=pdf
Reply
vinod nambiar
Active In SP
**

Posts: 2
Joined: Mar 2010
#3
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@gmail.com
Reply
project topics
Active In SP
**

Posts: 2,492
Joined: Mar 2010
#4
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 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
Reply
srinivasom
Active In SP
**

Posts: 3
Joined: Dec 2010
#5
19-12-2010, 09:12 PM

plz send me c- trend project and implimentation code in java
Reply
seminar surveyer
Active In SP
**

Posts: 3,541
Joined: Sep 2010
#6
20-12-2010, 09:30 AM

hi
below thread contains details on 'C-Trend'

topicideashow-to-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends
Reply
dharmasai
Active In SP
**

Posts: 1
Joined: Jun 2011
#7
22-06-2011, 01:02 PM

i want the implementation of this paper clearly step by step
my mail id: praveendharmasai3@gmail.com
Reply
tweetyvina
Active In SP
**

Posts: 2
Joined: Jul 2011
#8
02-07-2011, 03:26 PM

please can any 1 send C-TREND ppt'ss to my ID rechercherhoney@gmail.com for my MTech project and implimentation reviews

vina
Reply
seminar addict
Super Moderator
******

Posts: 6,592
Joined: Jul 2011
#9
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

topicideashow-to-c-trend-temporal-cluster-graphs-for-identifying-and-visualizing-trends?pid=63598#pid63598
Reply

Important Note..!

If you are not satisfied with above reply ,..Please

ASK HERE

So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page

Quick Reply
Message
Type your reply to this message here.


Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
Star Mining Web Graphs for Recommendations Guest 1 372 21-09-2013, 09:23 AM
Last Post: seminar projects maker
  RECENT TREND IN NON-CONVENTIONAL ENERGY SOURCES : BIODIESEL FUEL FOR THE NEXT GENERAT dhandejk95@gmail.com 0 369 14-07-2013, 09:08 AM
Last Post: dhandejk95@gmail.com
  a link based cluster ensemble approach for categorical data clusterin Guest 0 345 05-02-2013, 02:19 PM
Last Post: Guest
  mining web graphs for recommendations Guest 4 1,562 21-12-2012, 05:51 PM
Last Post: project girl
  identifying ethical hacker project and implimentation chandana429 0 338 05-11-2012, 04:16 PM
Last Post: chandana429
  emerging trends in mechanical engg Guest 0 630 22-09-2012, 12:02 PM
Last Post: Guest
  ssl backend forwarding scheme in cluster-based web servers Guest 4 933 19-07-2012, 09:57 AM
Last Post: Prathmeshb1
  cluster chandana519 1 588 01-03-2012, 12:54 PM
Last Post: seminar paper
  Human Motion Tracking by Temporal-Spatial Local Gaussian Process Experts divyasree520 0 1,124 01-02-2012, 06:09 PM
Last Post: divyasree520
  C-Trend: Temporal Cluster GraphsFor Identifying And Visualizing Trends In Multiattri sadlekargaurivijay 1 723 25-01-2012, 10:54 AM
Last Post: seminar addict