Tree-Based Mining for Discovering Patterns of Human
Interaction in Meetings
ABSTRACT:
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.
SYSTEM ARCHITECTURE
LOGIN
Interactions
PC PURCHASE
TRIP PLANNING
SOCCER
JOB
Comment display and insert
Details, comments and idea stored to
Session Tree
Graph
Final Tree
LOGIN
Tree Based view
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 SYSTEM:
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.
PROPOSED SYSTEM:
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.
ALGORITHM:
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.
MODULES:
 Interaction Flow Construction Module
 Expressing Opinion Module
 Analysis Module
 Session Tree Module
 Graph Creation Module
 Final Tree Module
MODULES DESCRIPTION:
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
 Soccer
 Job
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.
ANALYSIS MODULE
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 REQUIREMENTS:
HARDWARE REQUIREMENTS:
• System
: Pentium IV 2.4 GHz.
• Hard Disk
: 40 GB.
• Floppy Drive
: 1.44 Mb.
• Monitor
: 15 VGA Colour.
• Mouse
: Logitech.
• Ram
: 512 Mb.
SOFTWARE REQUIREMENTS:
• Operating system : - Windows XP.
• Coding Language : ASP.NET, C#.Net.
• Data Base
: SQL Server 2005
REFERENCE:
Zhiwen Yu, Senior Member, IEEE, Zhiyong Yu, Xingshe Zhou, Member, IEEE,
Christian Becker, Member, IEEE, and Yuichi Nakamura, Member, IEEE, “TreeBased Mining for Discovering Patterns of Human Interaction in Meetings”, IEEE
TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL.
24, NO. 4, APRIL 2012.