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Sunday, August 12, 2012
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings
Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings
Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Treebased interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.
SCOPE OF THE PROJECT:
Data mining, which is a powerful method of discovering new knowledge, has been widely adopted in many fields, such as bioinformatics, marketing, and security. In this study, we investigate data mining techniques to detect and analyze frequent interaction patterns; we hope to discover various types of new knowledge on interactions.
Human interaction flow in a discussion session is represented as a tree. Inspired by tree-based mining, we designed interaction tree pattern mining algorithms to analyze tree structures and extract interaction flow patterns. An interaction flow that appears frequently reveals relationships between different types of interactions. Mining human interactions is important for accessing and understanding meeting content.
First, the mining results can be used for indexing meeting semantics, also existing meeting capture systems could use this technique as a smarter indexing tool to search and access particular semantics of the meetings.
Second, the extracted patterns are useful for interpreting human interaction in meetings. Cognitive science researchers could use them as domain knowledge for further analysis of human interaction. Moreover, the discovered patterns can be utilized to evaluate whether a meeting discussion is efficient and to compare two meeting discussions using interaction flow as a key feature.
Existing meeting capture systems could use this technique as a smarter indexing tool to search and access only particular semantics of the meetings. This work focuses on only lower level knowledge about human interaction. The process didn’t have any key features. So it not compares two meeting discussions. The process only gets the positive and negative comments from the users. So further process to be discussed only by the admin. So complex of the topic should not be identified easily. Sometimes this process not provides the semantic information and produces redundant data.
DISADVANTAGES OF EXISTING SYSTEM:
· Complex to handle.
· Identification of negative points in topic is very tough.
· It increases the repeated data.
We propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. The work focuses on discovering higher level knowledge about human interaction. In our proposed system T-pattern technique is used to discover hidden time patterns in human behavior. We conduct analysis on human interaction in meetings and address the problem of discovering interaction patterns from the perspective of data mining. It extracts simultaneously occurring patterns of primitive actions such as gaze and speech. We discover patterns of interaction flow from the perspective of tree-based mining rather than using simple statistics of frequency. The main features of the process are user can also provides the idea about the topic. So admin can easily solve the problem based on users needed.
ADVANTAGES OF PROPOSED SYSTEM:
· Easy to handle.
· It extracts data simultaneously.
· Problems occurred in the process is easily solved by the admin.
Stochastic Techniques: The visualization systems aim at detecting and visualizing human interactions in meetings, while our work focuses on discovering higher level knowledge about human interaction. There have been several works done in discovering human behavior patterns by using stochastic techniques. A Stochastic Techniques is a collection of random variables; this is often used to represent the evolution of some random value, or system, over time. This is the probabilistic counterpart to a deterministic process. Instead of describing a process which can only evolve in one way, in a stochastic or random process there is some indeterminacy: even if the initial condition is known, there are several directions in which the process may evolve.
Algorithms for Pattern Discovery: With the representation model and annotated interaction Flows, we generate a tree for each interaction flow and thus build a tree data set. For the purpose of pattern discovery, we first provide the definitions of a pattern and support for determining patterns. In developing our frequent sub tree discovery algorithm, we decided to follow the structure of the algorithm for pattern discovery used for finding frequent item sets, because it achieves the most effective pruning compared with other algorithms.
Tree pattern mining algorithms: To analyze tree structures and extract interaction flow patterns. An interaction flow that appears frequently reveals relationships between different types of interactions. Mining human interactions is important for accessing and understanding meeting content. A tree-based mining method is used for discovering frequent patterns of human interaction in meeting discussions. The mining results would be useful for summarization, indexing, and comparison of meeting records. They also can be used for interpretation of human interaction in meetings.
· Interaction Flow Construction Module
· Expressing Opinion Module
· Analysis Module
· Session Tree Module
· Graph Creation Module
· Final Tree Module
Interaction Flow Construction
This is the first module, where we create an environment for interaction and flow construction. Based on the interaction defined and recognized, we now describe the notion of interaction flow and its construction. An interaction flow is a list of all interactions in a discussion session with triggering relationship between them. We create an application based on it. In the application we have authentication process. For authentication process we build Login process, which is used for enter the process and register the new users. This process is produced for both users and admin process. All users details can be stored in the database elements. So, unwanted users cannot easily access this Login process. Homepage is used for the login. Registration process requires the Name, Details, address, phone number and email id.
EXPRESSING OPINION MODULE
Comments display process is the process of displaying the comments in the user and admin view. But users view is different from the admin view. In user view, the user can view the comments and also enter the comment elements. This comment display is classified by four meeting display in our project are
· Home page
· PC Purchase
· Trip Planning
These processes get the details about the users and get the idea for the topics and also negative and positive comments. Users can view the positive and negative comments of other users are used. So currents users can get the knowledge about that particular topic and also they correct the doubts in their topics.
Admin process is the process that maintains the process and users details. The Main details of the users can not viewed by users, that type of process is maintained by the admin process. Admin process can view the process as tree based structure. So the admin can easily identified by the human interactions. Human interaction process can be viewed by admin by the following structure based elements are various elements which are described in the following modules.
SESSION TREE MODULE:
Session tree process is the process that is used to avoid the repeated data in session database. And the process provides the tree based structure. So the admin identified the main problem in the particular topic. All process such as PC purchase, Trip planning, Soccer and job can be viewed by the admin process.
GRAPH CREATION MODULE:
Graph is another process for the admin view. This process is also related to the session tree concept. But is process only provides the separate graph view. So the admin can easily maintain the process.
FINAL TREE MODULE:
Final Tree is the process that is fully related to the session tree and graph. This process provides the full view of the user interaction. So details of all users can be easily identified by the admin process.
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 512 Mb.
• Operating system : - Windows XP.
• Coding Language : ASP.NET, C#.Net.
• Data Base : SQL Server 2008
Zhiwen Yu, Senior Member, IEEE, Zhiyong Yu, Xingshe Zhou, Member, IEEE,
Christian Becker, Member, IEEE, and Yuichi Nakamura, Member, IEEE, “Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 4, APRIL 2012.