Computers and Software

ISSN 1828-6003 Vol. 8 N. 11 November 2013

International Review on

Computers and Software (IRECOS) Contents: Mining the Change of Customer Behavior in Fuzzy Time-Interval Sequential Patterns with Aid of Similarity Computation Index (SCI) and Genetic Algorithm (GA) by L. Mary Gladence, T. Ravi

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RFID Data Encoding Scheme in Supply Chain Management with Aid of Orthogonal Transformation and Genetic Algorithm (GA) by Maria Anu V., G. S. Anandha Mala

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Tree-Based Weighted Interesting Pattern Mining Approach for Human Interaction Pattern Discovery by S. Uma, J. Suguna

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FCM-FCS: Hybridization of Fractional Cuckoo Search with FCM for High Dimensional Data Clustering Process by Golda George, Latha Parthiban

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Hybrid Model Based Feature Selection Approach Using Kernel PCA for Large Datasets by J. Vandar Kuzhali, S. Vengataasalam

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Review on Software Metrics Thresholds for Object-Oriented Software by Abubakar D. Bakar, Abu B. Sultan, Hazura Zulzalil, Jamilah Din

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Managing Software Project Risks (Design Phase) with Proposed Fuzzy Regression Analysis Techniques with Fuzzy Concepts by Abdelrafe Elzamly, Burairah Hussin

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A Cluster Based Routing Protocol with Mobility Prediction for Mobile Sensor Networks by Sachin Paranjape, Mukul Sutaone

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Propose Approach for UDP Random and Sequential Scanning Detection Based on the Connection Failure Messages by Mohammed Anbar, Sureswaran Ramadass, Selvakumar Manickam, Alhamza Munther, Esraa Alomari

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Interlinking of Communication Protocols Through WAP Gateway Technologies Using Network Simulator by K. Muruganandam, V. Palanisamy

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Performance Analysis of Cross Layer Communication in Wireless Sensor Network to Improve Throughput and Utility Maximization by K. Kalai Kumar, E. Baburaj

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Sem-Rank: a Page Rank Algorithm Based on Semantic Relevancy for Efficient Web Search by V. Vijayadeepa, D. K. Ghosh

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International Review on Computers and Software (I.RE.CO.S.), Vol. 8, N. 11 ISSN 1828-6003 November 2013

Tree-Based Weighted Interesting Pattern Mining Approach for Human Interaction Pattern Discovery S. Uma1, J. Suguna2 Abstract – Human Interaction is an essential aspect to recognize communicative data in various applications including medical diagnosis and human-computer interaction. Mining human Interaction in meetings is very helpful to determine reactions of persons in different scenarios. Behavior denotes the nature of the person and mining helps to examine the exhibition of one’s opinion. This research work presents a novel technique to mine frequent patterns of human interaction based on the meetings. In this proposed approach, human interaction flow in a discussion session is denoted as a tree. Tree-based interaction mining of Weighted Interesting Pattern mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results shows that several interesting patterns are efficiently extracted which 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. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Pattern Discovery, Human Interaction, Weighted Interesting Pattern Mining, Tree Structure

I.

Introduction

The meeting is one of the important factors in the development of business and it can be successful when it is well organized. The meeting is well organized or not can be determined by finding the human behavior or human interaction in the meeting. The interaction between humans and its flow in the meeting can be found out by using the several efficient methods [1]. Some of the interaction flow which is frequently used in the meeting can be found out by using the efficient methods. Usually the interaction flow in the meeting such as interactions flow occur often, interaction flow discussion usually follows, relationship between exist among interactions and it is called as higher level of semantic knowledge. The higher level of semantic knowledge about the meeting usually used to illustrate the important pattern of interaction. The formation of well defined dictionary for the relevant meetings is the challenging the problem and it is formed based on the people interaction. The pattern used in the existing method can be used to calculate the efficiency of the meetings and comparing the meetings. Many researchers have been going on for the past decade and captured the pattern of this informal meeting. Mining the information from the database and discovering the new patterns by using data mining. Data mining is widely used in many fields for classifying the data [2], [3], [4] clustering the patterns.

Manuscript received and revised October 2013, accepted November 2013

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Many data mining techniques are used to find the patterns from the meetings. The frequently used pattern in the meeting is used to evaluate efficiency of the meeting by calculating. The parameters such as the effectiveness of the decision made in the meetings, by estimating the meeting discussion is successful or not and comparing the two meetings by using the interaction flow as a key feature [5]. The human interactions can be discovered by accessing and understanding the content of the meeting. The discovered patterns are used as a indexing tool for searching and accessing the particular semantics in the meetings [6], [7]. The pattern from the meeting is useful for prediction of the human behavior in the meetings. Many researches has been used this as a domain knowledge for study of human behavior.

II.

Related Work

Many novel algorithms are proposed by many researchers for mining the frequent patterns in recent times. Zaki in 2005 is proposed a novel algorithm based on the tree called as TREEMINER [8] with the help of new data structure called as scope-list. The efficiency of the algorithm can be evaluated by performing the several steps and proving that the proposed algorithm is better than the other existing systems in all ways. The proposed algorithm is used to analyze the real web logs by creating application for usage patterns.

Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

S. Uma, J. Suguna

The pattern growth for algorithm is developed by XSpanner and chopper [9] for mining frequent tree pattern. The problem of the existing algorithm leads to the development of the novel algorithm TREEMINERV. Although the database contains the several complex patterns, it is an efficient and scalable. The human interaction or behavior can be described dynamically by Waibel et al. [10]. The hidden markov model used for discovering the frequent patterns based on the human behavior and group actions are explained as a two layer process which is proposed by McCowan et al. [11]. Otsuka et al. [12] proposed an model for discovering a human interaction pattern based on the face, gaze, head gestures and utterance to determine who responds to whom. Yu et al. [13] proposed a model based on the tree based mining for discovering human interaction pattern and it mainly focused on the parent – child relationship. Many methods and system is used for capturing human interactions pattern by using multimedia using rear view, image processing, and computer visualization [14]. It is also used to form group activities such as staff meetings design discussions, project reviews, videoconferences, presentations, and classes. Sometimes meeting is recorded for further purpose either in the handwritten material or else recorded as a video and audio. Sometimes the patterns are discovered from the recorded video or audio [15]. Many researches and studies have been conducted in the field of image processing, speech processing, computer vision and human computer interaction based on the human interaction pattern [16], [17]. The pattern discover of human interaction have several issues such as turn-taking, gaze behavior, influence, and talkativeness. To overcome these problems the approach is proposed and it is called as AMI project. The efficiency of the meeting can be calculated by reviewing the group interactions dynamically and it is mainly used for discovering the pattern.

III.2. Representation of Interaction Flow The various interactions are propose, comment, acknowledgement, requestInfo, askOpinion, posOpinion, and negOpinion. The triggering relationships between the interactions are assembled as a list in a discussion session. It is explained in detail manner. Propose: Novel ideas or opinions are shared by participant. askOpinion: The opinions are asked from the participants for effective meeting. posOpinion: Sharing the positive attitude or opinion for the proposed idea by participant. negOpinion: Sharing the negative attitude or opinion for the proposed idea by participant. Acknowledgement: The participant agrees or disagree the proposal or idea. Comment: Commenting about others interactions or behavior. Requestinformation: The participant requesting the information regarding the idea or issue. The human interactions pattern are discovered based on the following variety of features such as gestures, attention, speech tone, speaking time, interaction occasion, and information about the previous interaction. The classification in patterns discovery are based on the support vector machine, Bayesian Net, Naı¨ve Bayes, and Decision Tree. Four methods are compared by classifying the data’s and the results shows that SVM outperforms the other. The recognition rate is used as one of the parameter for calculating the efficiency of the classifiers and SVM achieves the 80 percent of recognition rate as a results. As mentioned in the previous papers capturing the human interactions by recording as video which is used as a video annotation tool with user defined hierarchical layers and attributes. III.3. Interaction Flow Construction

III. Human Interactions and Interaction Flow

The parameters used to construct the interaction flow in this paper are defined below:

III.1. Human Interaction Definition and Recognition

Definition 1 (Session). A session is a unit of a meeting that initiates with a natural interaction and concludes with an interaction that is not followed by any reactive interactions. In session, the interactions are normally classified as a spontaneous interactions and reactive interactions. The person spontaneously initiates the interactions and the triggered response to another interaction is reactive interactions. In this paper the spontaneous interactions are propose and askOpinion whereas acknowledgement is a reactive interaction. The interaction types are identified by using the label which is annotated manually. The group of session is meeting and each and every session consists of one interaction types. The interaction pattern is formed by identifying the annotations, sessions. The main objects in this paper used for pattern mining is interaction flow.

The human behavior is defined as social or communicative actions by reviewing their behavior or various interactions in the meeting or group discussion. Various interactions such as different user roles, attitudes, and intentions are reviewed during the discussion. The interaction in the meetings or group discussions has various definitions such as task oriented interactions and etc. In this paper task oriented interactions are used for pattern discoveries that are used for task related aspect [18]. Data mining techniques are used to discover the patterns, analyze the interaction pattern. The pattern mining is based on the tree based mining algorithm used to review the structure of the parent child relationship in tree.


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PRO

PRO

PRO

COM

COM

ACK

COM

ACK

ACK (c)

(b)

(a)

COM

COM

ACK

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Trigger

Figs. 1. Examples of tree representation of interaction flow

PRO

PRO

ACK

COM

COM

ACK

ACK

ACK (b)

(a) Figs. 2. Isomorphic trees

III.4. Tree-Based Interaction Pattern Mining Representation A tree structure is utilized to represent an interaction flow and then implement a string for encoding the tree formally. Definition (Interaction Tree). The interaction pattern is discovered based on the tree based mining in which tree is represented as interaction flow. The nodes is rooted, directed and labeled are used to construct the tree in effective manner. Usually tree is represented in mathematical model as = ( , ) where V is a set of interactions represented as a = {0, 1, . . . } vertices. The interaction are connected by using the edges which is represented as a = {( , )| , ∈ , ≠ }. The parent child relationship in the tree is represented as ( , ) ∈ where is the parent of is a child of . More than one child is allowed for parent whereas each child has a single parent. The labels are used to mapping the vertices of the tree and it is defined as a ∶ → where = { , , . . . };and label is represented using node is defined as ∈ , ( ). The interactions are performed in the ordered manner ie., left ones first than the right ones [19].


The best examples for labeling in the tree based mining are interactions used in this paper are = { ; ; ; ; ; ; } i.e., — propose, —comment, —acknowledgement, — requestInfo, —askOpinion, —posOpinion, and −negOpinion. In figure above, circle represents are interactions, arrows represents the flow of the interaction. In tree sorting the labels in alphabetical manner are not possible because relationship between the interactions will break. In this paper for efficient method string coding method is used for interaction trees. Definition 3 (Tree String Code). In this paper the tree is constructed using the string encoding method which is denoted by tsc and any interaction flow is selected as a root. The parent child relationship is denoted using “ − ” “ ∗ ” and the descendent is separated by using paraenthesis from others. The best example is − , − ( − )_ , − ( − _ )_ _ represented in the fig1 respectively. Definition 4 (Tree Preorder Sequence). The traversal ( ) of the tree is used to find the efficient interaction pattern in meeting. In this paper International Review on Computers and Software, Vol. 8, N. 11

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depth first preorder traversal label is used which is represented as a tps and " − " is used to connected the label sequence. The tree traversal definition is used in the Figs. 1 and represented as a − , − − − , − − − − − , respectively. Definition 5 (Isomorphic Tree). In this paper two tress are represented as a = ( , ) = ( , ), ( ) ≠ ( ) the places of the siblings are changed. Even ( ) = though the siblings places are changed ( ), in the tree, it will not affect the tree at any cost. Therefore and are called as isomorphic trees. The isomorphic trees are same tree structure is used to find out by using the temporal independence in the original interaction trees. Isomorphic trees are represented in this paper in Figs. 2. The preorder sequence of the two trees becomes same through commutation process even though preorder sequences are different ( − − − and − − − ). Definition 6 (Interaction Subtree). Given a tree = ( , ),tree ′ = ( ′, ′) is called a subtree of , denoted as ′ ⊆ , if 1. ′ ⊆ ; 2. ′ ⊆ ; 3. ′, ( ) = ′( ),

Notation TD ITD t tk Ck Fk σ

TABLE I NOTATION Description A dataset of interaction trees The full set of isomorphic trees to TD A tree A subtree with k nodes, i.e., k-subtree A set of candidates with k nodes A set of frequent k-subtrees A support threshold minsup

Algorithm for pattern discovery The pattern is discovered by using many data mining methods for efficient results. In this paper weighted interesting pattern mining is used for discovering the pattern by calculating the weight confidence and the similar pattern are grouped by using the similar weights. The parameters of the WIP algorithm used to mining the patterns are weight range based on the weight boundaries, h-confidence based on the strong support affinity patterns. The proposed algorithm considers the balance between weight and support measures of the items, weight affinity and support affinity of the items. The results of the WIP algorithm based pattern mining can generate the valuable patterns more effectively. III.5. WIP (Weighted Interesting Pattern mining) In WIP [15], the main approach of WIP is to push weight confidence and/or h-confidence into the weighted frequent pattern mining algorithm based on the pattern growth approach and prune uninteresting patterns. The whole weight affinity of the pattern is calculated based


on the level of the weight or support of the items within the pattern [20]. WIP Algorithm The pattern is discovered based on the methodologies used in the WIP algorithm they are ascending weight order method and bottom up traversal strategy. The weighted hyperclique patterns are generated based on the w confidence in the proposed algorithm [21, 22]. The interesting patterns are mined based on the algorithm explained briefly weight interesting pattern mining process. WIP algorithm: mining Weighted Interesting Patterns with a weight confidence and/or an h-confidence Input: (1) A transaction database: , (2) Minimum support: _ , (3) Weights of the items within weight range: , (4) Minimum w-confidence: _ (5) Minimum h-confidence: _ℎ Output: The complete set of weighted hyperclique patterns. Begin 1. Let WIP be the set of weighted interesting patterns that satisfy the constraints. Initialize WIP O {}; 2. Scan TDB once to find the global weighted frequent items satisfying the following definition: A pattern is a weighted frequent pattern if the following pruning condition is not satisfied. Pruning condition: ( ∗ < _ ). In a transaction database, the value of multiplying the support of a pattern with a MaxW of each item in the transaction database is less than a minimum support. 3. Sort items of WIP in weight ascending order. The sorted weighted frequent item list forms the weighted frequent list. 4. Scan the TDB again and build a global FP-tree using weight_order. 5. Call WIP (FP-tree, {}, WIP) Procedure WIP (Tree, R, WIP) 1: For each ai in the header of Tree do 2: set = 3: Get a set of items to be included in conditional database, ; 4: For each item in , Compute its count in conditional database; 5: For each bj in do 6: If (sub ( bj) * MinW < min_support) delete bj from ; 7: If (wconf ( bj) < min_wconf) delete bj from ; 8: If (hconf ( bj) < min_hconf) delete bj from ; 9: End for 10: ← _ _ ( , ) 11: If ≠ 0 then 12: Call WIP ( , , WIP) 13: End if 14: End for


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IV.

Experimental Results

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The simulation of the pattern discovery of human interaction based on weight interesting pattern mining algorithm created in either MATLAB.

Frequent pattern mining

140 120

weighted interesting pattern mining

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100 80

IV.1. Data Set

60 40 20

session percentage

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4000 2000 0 0

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Fig. 4. Support threshold and running time

Running time(ms)

The four real meetings are organized based on the purchasing, trip-planning, soccer preparation and job selection. Each and every meeting consist of four participants are seated and duration is maximum 20minutes. The multimodal and recognition methods along with the WIP algorithm are used for fine tuning of the interaction types which is recorded either by video or audio. Based on the interaction patterns which comprises of 1406 interactions and 356 sessions, 356 interaction trees are obtained. In Fig. 3 represents the interactions discovered in the meeting. It mainly consists of three interactions and the longest one consists of 14 interactions. The sessions are considered as a special if it contains 9 to 14 interactions whereas other sessions are considered as normal ones. The examples for the longest sessions are PRO-PROACK_(COM-(COMACK_(COM-COM-COM-(COMCOM -COM)_REQ))_COM),” “COM-(COMREQ_(COM-ACK-COM-COM(COM-COMCOM_(NEG-COM))_COM))_ ACK,” and “COM(COM-ACK_ACK _ACK_ACK _ACK)_ ACK_ACK_ ACK_ACK_ACK_ACK_ACK,”. It is represented in this paper based on the tree string code.

proposed

Conclusion

The human interaction patterns are discovered by using the novel algorithm weight interesting pattern (WIP). Several real meetings are conducted for obtaining the real data’s. The interesting patterns are obtained based in the real data sets and the weight measure, support confidences are calculated for mining the patterns. The proposed method is compared with the existing approaches and results are shown the graphical manner. The proposed methods outperform the other existing method in all ways.

10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Number of Iterations in one Session Fig. 3. Session distribution

The simulations results are observed and concluded that session consist of comments proposals and questions. In the interaction pattern comment appears five times and acknowledgement appears four times in the example meeting. Based on the threshold value the frequent patterns are mined and results are shown as graph in the Fig. 4. The support threshold and running time are inversely proportional. The 139 frequent subtrees were discovered and graph is constructed based on the values obtained for frequent sub trees and support threshold. The frequent subtrees are inversely proportional to support threshold. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

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Authors’ information 1

Assistant Professor, School of Computer Science, C.M.S.College of Science & Commerce. E-mail: [email protected] 2

Associate Professor, Dept of Computer Science, Vellalar College for Women, Erode. E-mail: [email protected] S. Uma received master’s degree in Information Technology from Bharathidasan University, Trichy in 2004 and M.Phil degree in Computer Science from Bharathidasan University, Trichy in 2007. She is currently an Assistant Professor in School of Computer Science, C.M.S.College of Science & Commerce (Autonomous), Coimbatore, Tamil Nadu. Her research interests are Data Mining and Image Processing. Dr. J. Suguna received the master’s degree in mathematics from Annamalai University, Chidambaram in 1988 and the Ph.D. degree in computer science from the Bharathiar University, Coimbatore in 2009. She is currently an Associate Professor with the Department of Computer Science and Controller of Examinations,Vellalar College for Women (Autonomous), Erode, Tamil Nadu. Her research interests are AI, Data Mining, Text Mining and Image Processing. She is the author or co-author of over 20 publications in journals, conference proceedings and book chapters. She has presented a paper in an International Conference held at Cincinnati University, Cincinati, Ohio, USA. She has produced over 15 M.Phil. Scholars in Computer Science.


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