A SERIAL COMPUTING MODEL OF AGENT ENABLED MINING OF GLOBALLY STRONG ASSOCIATION RULES

International Journal on Computational Sciences & Applications (IJCSA) Vol.5, No.3, June 2015
DOI:10.5121/ijcsa.2015.5307 77
A SERIAL COMPUTING MODEL OF AGENT
ENABLED MINING OF GLOBALLY STRONG
ASSOCIATION RULES
G.S.Bhamra1
, A. K.Verma2
and R.B.Patel3
1
M. M. University, Mullana, Haryana, 133207 - India
2
Thapar University, Patiala, Punjab, 147004- India
3
Chandigarh College of Engineering & Technology, Chandigarh- 160019- India
ABSTRACT
The intelligent agent based model is a popular approach in constructing Distributed Data Mining (DDM)
systems to address scalable mining over large scale and ever increasing distributed data. In an agent based
distributed system, variety of agents coordinate and communicate with each other to perform the various
tasks of the Data Mining (DM) process. In this study a serial computing mode of a multi-agent system
(MAS) called Agent enabled Mining of Globally Strong Association Rules (AeMGSAR) is presented based
on the serial itinerary of the mobile agents. A Running environment is also designed for the implementation
and performance study of AeMGSAR system.
KEYWORDS
Knowledge Discovery, Association Rules, Intelligent Agents, Multi-Agent System
1.INTRODUCTION
Data Mining (DM) technique is used to extract some interesting and valid data patterns implicitly
stored in large databases [1], [2]. Intelligent software agent technology is an interdisciplinary
technology dealing with the development and efficient utilization of autonomous software objects
called agents which have access to geographically distributed and heterogeneous resources. They
are autonomous, adaptive, reactive, pro-active, social, cooperative, collaborative and flexible.
They also support temporal continuity and mobility within the network. An intelligent agent with
mobility feature is known as Mobile Agent (MA). MA migrates from node to node in a
heterogeneous network without losing its operability. On reaching at a network node MA is
delivered to an Agent Execution Environment (AEE) where its executable parts are started
running. Upon completion of the desired task, it delivers the results to the home node. A Mobile
Agent Platform (MAP) or Agent Execution Environment (AEE), is a server application that
provides the appropriate functionality to MAs to authenticate, execute, communicate, migrate to
other platform, and use system resources in a secure way. A Multi Agent System (MAS) is
distributed application comprised of multiple interacting intelligent agent components [3].
Let { }, 1jDB T j D= = K be a transactional dataset of size D where each transaction T is assigned
an identifier (TID ) and { },i 1i
I d m= = K , total m data items in DB . A set of items in a particular
transaction T is called itemset or pattern. An itemset, { },i 1i
P d k= = K , which is a set of k data

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items in a particular transaction T and P I⊆ , is called k-itemset. Support of an itemset,
( )
No_of_T_containing_P
%s P
D
= is the frequency of occurrence of itemset P in DB , where
No_of_T_containing_P is the support count (sup_count) of itemset P . Frequent Itemsets (FIs)
are the itemset that appear in DB frequently, i.e., if ( ) min_th_sups P ≥ (given minimum
threshold support), then P is a frequent k-itemset. Finding such FIs plays an essential role in
miming the interesting relationships among itemsets. Frequent Itemset Mining (FIM) is the task
of finding the set of all the subsets of FIs in a transactional database [2].
Association Rules (ARs) are used to discover the associations among item in a database [4]. It is
an implication of the form [ ]support,confidenceP Q⇒ where, ,P I Q I⊂ ⊂ and P Q∩ = ∅ . An
AR is measured in terms of its support and confidence factor where support of the rule
( ( )s P Q⇒ ) is the probability of both P and Q appearing in T , i.e., ( )p P Q∪ and the
confidence of the rule ( ( )c P Q⇒ ) is the conditional probability of Q given P , i.e., ( )|p Q P .
An AR is said to be strong if ( ) min_th_sups P Q⇒ ≥ (given minimum threshold support) and
( ) min_th_confc P Q⇒ ≥ (given minimum threshold confidence). Association Rule Mining (ARM)
today is one of the most important aspects of DM tasks. In ARM all the strong ARs are generated
from the FIs. The ARM can be viewed as two step process [5], [6].
1. Find all the frequent k-itemsets ( k
L )
2. Generate Strong ARs from k
L
a. For each frequent itemset, k
l L∈ , generate all non empty subsets of l .
b. For every non empty subset s of l , output the rule “ ( )s l s⇒ − ”, if
( )
( )
sup_count
min_th_conf
sup_count
l
s
≥
Distributed Association Rule Mining (DARM) is the task of generating the globally strong
association rules from the global FIs in a distributed environment. Few preliminaries notations
and definitions required for defining DARM and to make this study self contained are as follows:
• { },i 1iS S n= = K , n distributed sites.
• CENTRAL
S , Central Site.
• { }, 1i j i
DB T j D= = K , Horizontally partitioned data set of size i
D at the local site i
S , where
each transaction j
T is assigned an identifier (TID).
• 1
n
ii
DB DB=
= U , the aggregated dataset of size 1
n
ii
D D=
= ∑ , i j
DB DB∩ = ∅
• { },i 1i
I d m= = K , total m data items in each i
DB .
• ( )
FI
k i
L , Local frequent k-itemsets at site i
S .
• ( )
FISC
k i
L , List of support count ( )
FI
k i
Itemset L∀ ∈ .
• LSAR
i
L , List of locally strong association rules at site i
S .
• 1
nTLSAR LSAR
ii
L L=
= U , List of total locally strong association rules.
• ( )1
nTFI FI
k k ii
L L=
= U , List of total frequent k-itemsets.

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• ( )1
nGFI FI
k k ii
L L=
= I , List of global frequent k-itemsets.
• GSAR
CENTRAL
L , List of Globally strong association rule.
Local Knowledge Base (LKB), at site iS , comprises of ( )
FI
k i
L , ( )
FISC
k i
L and LSAR
i
L which can provide
reference to the local supervisor for local decisions. Global Knowledge Base (GKB), at CENTRAL
S ,
comprises of TLSAR
L , TFI
k
L , GFI
k
L and GSAR
CENTRAL
L for the global decision making [7]. Like ARM, DARM
task can also be viewed as two-step process [6]:
1. Find the global frequent k-itemset ( GFI
k
L ) from the distributed Local frequent k-itemsets
( ( )
FI
k i
L ) from the partitioned datasets.
2. Generate globally strong association rules ( GSAR
CENTRAL
L ) from GFI
kL .
The existing agent based systems specifically dealing with DARM task are: Knowledge
Discovery Management System (KDMS) [8], Efficient Distributed Data Mining using Intelligent
Agents [9], Mobile Agent based Distributed Data Mining [10], An Agent based Framework for
Association Rule Mining of Distributed Data (AFARMDD) [11], [12], Multi-Agent Distributed
Association Rule Miner (MADARM) [13]. All these systems are academic research projects.
Qualitative comparison of these DARM frameworks is provided in [14]. Most of the existing
agent based frameworks for DARM task are only prototype model and lacks the appropriate
underlying AEE, scalability, privacy preserving techniques, global knowledge generation and
implementation using a real datasets.
The rest of the paper is organised as follows. Section 2 described the running environment for the
proposed system along with various algorithms involved. Serial computing model of AeMGSAR
is presented in Section 3. Algorithms for all the agents involved in this system are also discussed.
Section 4 describes the implementation and performance study of the system and finally the
article is concluded in Section 5.
2.ENVIRONMENT FOR THE PROPOSED SYSTEM
Every MAS needs an underlying AEE to provide a running infrastructure on which agents can be
deployed and tested. A running environment has been designed in Java. Various attributes of the
MA are encapsulated within a data structure known as AgentProfile . It contains the name of MA
( AgentName ), version number ( AgentVersion ), entire byte code ( BC ), list of nodes to be
visited by MA, i.e., itinerary plan ( NODESL ) , type of the itinerary ( ItinType ) which can be
serial or parallel, a reference of current execution state ( AObject ) and an additional data structure
known as Briefcase that acts as a result bag of MA to store final resultant knowledge ( iResult_S )
at a particular site. Computational time (CPUTime ) taken by a MA at a particular site is also
stored in iResult_S . In addition to results, Briefcase also contains the system time for start of
agent journey ( startTripTime ), system time for end of journey ( endTripTime ) and total round trip
time of MA (TripTime ) calculated using end startTripTime TripTime TripTime← − . Stationary as well
as mobile agents involved in the models would be discussed later on. This environment consists
of the following three components:

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• Data Mining Agent Execution Environment (DM_AEE): It is the key component that
acts as a Server. DM_AEE is deployed on any distributed sites iS and is responsible for
receiving, executing and migrating all the visiting DM agents. It receives the incoming
AgentProfile at site iS , retrieves the entire BC of agent and save it with
.AgentName class in the local file system of the site iS after that execution of the agent is
started using AObject . Steps are shown in Algorithm 1.
• Agent Launcher (AL): It acts a Client at agent launching station ( CENTRAL
S ) and launches
the goal oriented DM agents on behalf of the user through a user interface to the
DM_AEE running at the distributed sites. Agent Pool (or Zone) at CENTRAL
S is a repository
of all mobile as well as stationary agents (SAs). AL first reads and stores AgentName
in AgentProfile . The entire BC of the AgentName is loaded from the Agent Pool and
stored in AgentProfile . NODES
L and ItinType are retrieved and stored in AgentProfile .
startTripTime is maintained in Briefcase which is further added to AgentProfile . In case of
serial computing model, i.e., if ItinType Serial= , AL dispatches a specific single MA
along with NODES
L , and it travels from node to node. AgentVersion is set as 1 for this
agent. AL also contacts the Result Manager (RM) for processing the Briefcase of an agent.
Detailed steps are given in Algorithm 2.
• Result Manager (RM): It manages and processes the Briefcase of all MAs. RM is either
contacted by a MA for submitting its results or by AL for processing the results of the
specific MA. On completion of itinerary, each DM agent submits its results to RM which
computes total round trip time (TripTime ) of that MA and saves it in the Briefcase of that
agent. It ItinType Serial= then it saves the updated AgentProfile of an agent at CENTRALS .
When it is contacted by AL for processing the results of a specific agent it sends back the
AgentProfile of that agent. Steps are defined in Algorithm 3.
Algortihm 1 DATA MINING AGENT EXECUTION ENVIRONMENT (DM_AEE)
1: procedure DM_AEE( )
2: while TRUE do
3: iAgentPofile listen and receive AgentProfile at S←
4: AgentName get AgentName from AgentProfile←
5: BC retrieve the BC of agent from AgentProfile←
6: isave the BC with AgentName.class in the local file system of S
7: AObject get AObject from AgentProfile← >current state
8: . ()AObject run >start executing mobile agent
9: end while
10: end procedure
Algortihm 2 AGENT LAUNCHER (AL)
1: procedure AL( )
2: option read option(dispatch / result)←
3: switch option do
4: case dispatch >dispatch the mobile agent to DM_AEE
5: AgentName read Mobile Agent's name←
6: add AgentName to AgentProfile

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7: BC load entire byte code of AgentName from AgentPool←
8: add BC to AgentProfile
9: NODES
L read Itinerary (IP addresses)of mobile agent←
10: ItinType read ItinType( Serial / Parallel)←
11: add ItinType to AgentProfile
12: if " "ItinType Serial= then >Serial Itinerary
13: 1AgentVersion ←
14: add AgentVersion to AgentProfile
15: NODES
add L to AgentProfile
16: switch AgentName do
17: case LFIGA
18: minthrsup read minimum threshold support←
19: AObject new LFIGA(AgentProfile,minthrsup)←
20: end case
21: case LKGA
22: minthrconf read minimum threshold confidence←
23: AObject new LKGA(AgentProfile,minthrconf)←
24: end case
25: case TFICA
26: AObject newTFICA(AgentProfile)←
27: end case
28: case LKCA
29: (AObject new LKCA AgentProfile)←
30: end case
31: case GKDA
32: GSAR GSAR
CENTRAL CENTRAL CENTRALL load L generated by GKGA at S←
33: GSAR
CENTRALadd L to Briefcase
34: add updated Briefcase to AgentProfile
35: AObject newGKDA(AgentProfile)←
36: end case
37: end switch
38: add AObject to AgentProfile >current state
39: NODES
Transfer AgentProfile to DM_AEE at first IP address in L
40: end if
41: end case
42: case result >process the result of mobile agent
43: AgentName read mobile agent's name←
44: ItinType read mobile agent's ItinType←
45: AgentInfo
add AgentName to L
46: AgentInfo
add ItinType to L
47: > Result processing for Serial Itinerary Agents
48: if " "ItinType Serial= then
49: AgentInfo
AgentProfile contact RM for L←
50: Briefcase retrieve Briefcase from AgentProfile←
51: switch AgentName do
52: case LFIGA

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53: process the Briefcase of LFIGA
54: end case
55: case LKGA
56: process the Briefcase of LKGA
57: end case
58: case TFICA
59: call GFIGA(Briefcase) >stationary agent
60: end case
61: case LKCA
62: call GKGA(Briefcase) >stationary agent
63: end case
64: case GKDA
65: process the Briefcase of GKDA
66: end case
67: end switch
68: end if
69: end case
70: end switch
71: end procedure
Algortihm 3 RESULT MANAGER (RM)
1: procedure RM( )
2: while TRUE do
3: listen and receive the incomming request
4: if icontacted by a mobile agent for submitting results from site S then
5: iAgentProfile receive the incomming AgentProfile from site S←
6: ItinType retrieve ItinType from AgentProfile←
7: Briefcase retrieve mobile agent's Briefcase from AgentProfile←
8: start startTripTime retrieveTripTime from Briefcase←
9: end endTripTime retrieveTripTime from Briefcase←
10: end startTripTime TripTime TripTime← −
11: add TripTime to Briefcase
14: CENTRALsave AgentProfile at S
15: end if
16: end if
17: if contacted by AL for processing the results then
18: AgentInfo
AgentName retrieve AgentName from incomming L←
19: AgentInfo
ItinType retrieve ItinType from incomming L←
21: CENTRALAgentProfile load AgentProfile for AgentName from S←
22: dispatch AgentProfile to AL
23: end if
24: end if
25: end while
26: end procedure

83
The overall working of AeMGSAR system may be divided into following six stages:
1. Request Stage: Request for the DARM is initiated at CENTRALS by AL on behalf of the user
with necessary credentials.
2. Preparation Stage: AL through User Interface reads agent name; version number;
Itinerary for the MAs journey is obtained in terms of IP addresses of the distributed nodes
to be visited by a MA; any specific additional data for a specific MA is obtained; Agent
code for the specific MA is loaded from AgentPool; for serial itinerary a single specific
MA is dispatched by AL to travel and visit n distributed sites in parallel.
3. Local Mining Stage: ARM process is performed locally by specific DM agents on each
distributed site and results are kept as local knowledge base at that site.
4. Result Collection Stage: Collector agents visits each site and collect the results generated
by DM agents and submit the results back to RM at CENTRALS .
5. Knowledge Integration and Global Knowledge Generation Stage: Knowledge or result
integration is carried out by the RM with the help of stationary agent and Global
Knowledge in the form of Globally Strong Association Rules may be generated with the
help of other stationary agents at CENTRALS .
6. Global Knowledge Dispatching Stage: Global knowledge is dispatched to the distributed
sites by a dispatching agent to compare it with the local knowledge at each site.
Figure 1. AeMGSAR Serial Computing Model

84
3.SERIAL COMPUTING MODEL OF AEMGSAR
Serial computing model of AeMGSAR system is shown in Figure 1. It consists of total seven
agents, five of these are MAs dispatched from CENTRALS with serial itinerary multi-hop migration
and other two are intelligent SAs running at CENTRALS to perform different tasks. The CPU time
taken by a MA while processing on each site along with some other specific information is
carried back in the result bag at CENTRALS . Agents in serial number 1-5 visit n sites serially other
parameters are collected from different resources. Detailed relationship among these agents and
working behaviour of each agent is as follows:
1. Local Frequent Itemset Generater Agent (LFIGA): This is a MA that carries the
AgentProfile & min_th_sup . LFIGA generates and stores ( )
FI
k iL and ( )
FISC
k iL at site iS by
scanning the local iDB at that site with the constraint of min_th_sup . It carries back the
computational time (CPUTime ) at each site iS and endTripTime . This agent is embedded
with Apriori algorithm [15] for generating all the frequent k-itemset lists. It may be
equipped with decision making capability to select other FIM algorithms based on the
density of the dataset at a particular site. More details are available in Algorithm 4.
2. Local Knowledge Generater Agent (LKGA): This is a MA that carries the
AgentProfile & min_th_conf . LKGA applies the constraint of min_th_conf to generate and
store LSAR
iL by using the ( )
FI
k iL and ( )
FISC
k iL lists already generated by LFIGA agent at site iS .
LSAR
iL list also support and confidence for a particular association rule along with the site
name. It carries back the computational time (CPUTime ) at each site iS and endTripTime .
Detailed steps are given in Algorithm 7.
3. Total Frequent Itemset Collector Agent (TFICA): This is a MA that carries the
AgentProfile . TFICA collects list of local frequent k-itemset ( ( )
FI
k iL ) generated by LFIGA
agent and carries back the list of total frequent k-itemset TFI
kL in the result bag to RM at
CENTRALS . In addition to this resultant knowledge, it also carries back the computational
time (CPUTime ) at each site iS and endTripTime . It executes Algorithm 8.
4. Local Knowledge Collctor Agent (LKCA): This is a MA that carries the AgentProfile .
LKCA collects the list of locally strong association rules ( LSAR
iL ) generated by LKGA
agent and carries back the list of total locally strong association rules ( TLSAR
L ) in the result
bag to RM at CENTRALS . In addition to this resultant knowledge, it also carries back the
computational time (CPUTime ) at each site iS and endTripTime . Steps are shown in
Algprithm 9.
5. Global Knowledge Dispatcher Agent (GKDA): This is a MA that carries the
AgentProfile containing global knowledge ( GSAR
CENTRALL ). It dispatches global knowledge at
every site for further decision making and comparing with the local knowledge at that
site. It executes Algorithm 12.
6. Global Frequent Itemset Generater Agent (GFIGA): It is a stationary agent at CENTRALS ,
mainly used for processing the result bag of TFICA, i.e., total frequent k-itemset list
( TFI
kL ) generated y TIFCA to generate the global frequent itemset list, GFI
kL . More details
are available in Algorithm 10.
7. Global Knowledge Generater Agent (GKGA): It is also a stationary agent at CENTRALS ,
mainly used for processing the GFI
kL list and TLSAR
L list to compile the global knowledge,

85
i.e., the list of globally strong association rules, GSAR
CENTRALL . Detailed steps are shown in
Algorithm 11.
Algortihm 4 LOCAL FREQUENT ITEMSET GENERATER AGENT (LFIGA)
Input:
• AgentProfile,A collection of agent attributes set by the AL
• min_th_sup,the given minimum threshold support
Output: FI &SC
L ,the list of frequent itemsets and their support counts
1: procedure LFIGA( AgentProfile,min_th_sup )
2: startCPUTime get system time←
3: Briefcase get Briefcase from AgentProfile←
4: i i iDB load DB from local file system of site S←
5: . (0)iT DB get← >No. of records
6: . (1)iI DB get← >No. of items
7: . (3)iDB[T][I] DB get← >itemset data bank
8: minsupcount (T ×min_th_sup) / 100←
9: >generate frequent-1 itemset list ( 1FIL ) and support count list ( 1FISC )
10: 1CFIL {1,2,3...I}← >candidate frequent-1 itemset
11: for i 1,I← do >initialize the support count array 1SCFIL to zero
12: 01SCFIL [i] ←
13: end for
14: 1k ←
15: for all 1candidate c CFIL∈ do >find support count for every candidate
16: for all transaction t DB∈ do
17: if c t⊂ then
18: 1 1[ ] [ ] 1SCFIL k SCFIL k← +
19: end if
20: end for
21: 1k k← +
22: end for
23: >prune 1 1 1CFIL to generate FIL and FISC
24: for 1,k I← do
25: if 1[ ]SCFIL k minsupcount≥ then
26: k 1 1add c CFIL to FIL∈
27: 1 1add SCFIL [k] to FISC
28: end if
29: end for
30: if 1FIL ≠ ∅ then
31: FI
1add FIL to L
32: FISC
1add FISC to L
33: end if
34: 2k ←
35: while 1kFIL − ≠ ∅ do
36: k k-1CFIL Call GenerateCFIL(FIL )← >see Algorithm 5
37: for 1, .ki CFIL length← do >initialize the array kSCFIL to zero
38: [ ] 0kSCFIL i ←

86
39: end for
40: 1i ←
41: for all kcandidate c CFIL∈ do >find support count for every candidate
42: for all transaction t DB∈ do >scan DB
43: if c t⊂ then
44: 1 1[ ] [ ] 1SCFIL k SCFIL k← +
45: end if
46: end for
47: 1i i← +
48: end for
49: >prune kCFIL to generate kFIL and kFISC
50: for 1, .ki SCFIL length← do
51: if [i]kSCFIL minsupcount≥ then
52: i k kadd c CFIL to FIL∈
53: k kadd SCFIL [i] to FISC
54: end if
55: end for
56: if kFIL ≠ ∅ then
57: FI
kadd FIL to L
58: FISC
kadd FISC to L
59: end if
60: 1k k← +
61: end while
62: FI &SC
add T to L
63: FI FI &SC
add L to L
64: FISC FI &SC
add L to L
65: FI &SC
isave L in the local file system of this site S
66: endCPUTime get system time←
67: end startCPUTime CPUTime CPUTime← −
68: iadd CPUTime to Result_S
69: iadd Result_S to Briefcase
71: NODES
L get itinerary list from AgentProfile←
72: NODES NODES
L remove first IP address from L← >visited site
73: NODES
add updated L to AgentProfile
74: if NODES
L ≠ ∅ then >itinerary not empty
75: AObject new LGFIGA(AgentProfile,min_th_sup)←
76: add AObject to AgentProfile
77: NODES
transfer AgentProfile to DM_AEE at first IP address in L
78: else
79: endTripTime get system time for end of agent journey←
80: endadd TripTime to Briefcase
82: CENTRALtransfer AgentProfile to RM at S
83: end if
84: end procedure

87
Algortihm 5 GENERATECFIL
Input: 1,kL Frequent k -1itemsets−
Output: kC ,Candidate Frequent k itemsets
1: procedure GENERATECFIL ( 1kL − )
2: for all 1 k-1itemset l L∈ do
3: for all 2 k-1itemset l L∈ do
4: if 1 2 1 2 1 2(l [1] = l [1]) (l [2] = l [2]) (l [k -1] = l [k -1])∧ ∧ ∧L then
5: 1 2c l l← ⊗ >join step: generate candidates
6: end if
7: if HASINFREQUENTSUBSET ( 1, kc L − ) then >see Algorithm 6
8: delete c
9: else
10: kadd c to C
11: end if
12: end for
13: end for
14: return kC
15: end procedure
Algortihm 6 HASINFREQUENTSUBSET
Input: ,c Candidate k itemsets
Output: 1 1kL ,Frequent k itemsets− −
1: procedure HASINFREQUENTSUBSET ( 1, kc L − )
2: for all (k -1) subset s c∈ do
3: if 1ks L −∉ then
4: return TRUE
5: else
6: return FALSE
7: end if
8: end for
9: end procedure
Algortihm 7 LOCAL KNOWLEDGE GENERATER AGENT (LKGA)
Input:
• AgentProfile,A collection of agent attributes set by the AL
• min_th_conf,the given minimum threshold confidence
Output: LSAR
L ,the list of locally strong association rules
1: procedure LKGA( AgentProfile,min_th_conf )
4: FI &SC FI &SC
iL load L from local file system of this site S←
5: &
. (0)FI SC
T L get← >No. of records
6: &
. (1)FI FI SC
L L get← >frequent k-itemset list

88
7: &
. (2)FISC FI SC
L L get← >support count list
8: for 2, .FI
k L size← do
9: . ( )FI
kL L get k← >get frequent k-itemset list
10: for all kl L∈ do
11: subsetsl generate all non - empty subsets of l←
12: FISC
spcountl get support count of l from L←
13: (l / T) 100support spcountAR ← × >support of the association rule
14: for all subsetsnon - empty subset s l∈ do
15: FISC
spcounts get support count of s from L←
16: conf spcount spcountAR (l / s )×100← >confidence of the association rule
17: if confAR min_th_conf≥ then
18: strong support confAR "s l - s[AR %,AR %]"← ⇒
19: print strongAR
20: strongadd l to AR
21: IP
i iS get IP address of this site S←
22: IP
i strongadd S to AR
23: LSAR
strongadd AR to L
24: end if
25: end for
26: end for
27: end for
28: LSAR
isave L in the local file system of this site S
34: NODES
35: NODES NODES
36: NODES
37: if NODES
38: AObject new LKGA(AgentProfile,min_th_conf)←
40: NODES
41: else
46: end if
47: end procedure

89
Algortihm 8 TOTAL FREQUENT ITEMSET COLLECTOR AGENT (TFICA)
Input: AgentProfile,A collection of agent attributes set by the AL
Output: FI
L ,the list of locally frequent itemsets
1: procedure TFICA( AgentProfile,min_th_conf )
4: FI &SC FI &SC
5: &
. (1)FI FI SC
L L get← >frequent k-itemset list
6: FI
iadd L to Result_S
12: NODES
13: NODES NODES
14: NODES
15: if NODES
16: AObject newTFICA(AgentProfile)←
18: NODES
19: else
24: end if
25: end procedure
Algortihm 9 LOCAL KNOWLEDGE COLLECTOR AGENT (LKCA)
Output: LSAR
L ,the list of locally strong association rules
1: procedure LKCA( AgentProfile )
4: LSAR LSAR
5: LSAR
iadd L to Result_S

90
11: NODES
12: NODES NODES
13: NODES
14: if NODES
15: AObject new LKCA(AgentProfile)←
17: NODES
18: else
23: end if
24: end procedure
Algortihm 10 GLOBAL FREQUENT ITEMSET GENERATER AGENT (GFIGA)
Input: Briefcase, Result bag of TFICA agent
Output: GFI
L ,the list of global frequent itemsets
1: procedure GFIGA( Briefcase )
3: ( )nTFI FI
ii=1
L retrieve total frequent itemsets L from Briefcase← U
4: ( )1
nGFI FI
ii
L retrieve global frequent itemsets L from Briefcase=
← I
5: print GFI
L
6: GFI
CENTRALsave L in the local file system of site S
9: print CPUTime
10: return GFI
L
11: end procedure
Algortihm 11 GLOBAL KNOWLEDGE GENERATER AGENT (GKGA)
Input: Briefcase, Result bag of LKCA agent
Output: GSAR
CENTRALL ,the list of globally strong association rules
1: procedure GKGA( Briefcase )
3: ( )nTLSAR LSAR
ii=1
L retrieve total strong rules L from Briefcase← U
4: ( )GFI GFI
CENTRALL load global frequent itemsets L from S←
5: for all TLSAR
strongAR L∈ do
6: strongL get frequent itemset from AR←
7: if GFI
L L∈ then

91
8: print IP
strong iAR along with the site address (S )
9: GSAR
strong CENTRALadd AR to L
10: end if
11: end for
12: GSAR
CENTRAL CENTRALsave L in the local file system of site S
15: print CPUTime
16: return GSAR
CENTRALL
17: end procedure
Algortihm 12 GLOBAL KNOWLEDGE DISPATCHER AGENT (GKDA)
Output: GSAR
CENTRAL iDispatch L at each distributed site S
1: procedure GKDA( AgentProfile )
4: GSAR GSAR
CANTRAL CENTRALL get L from Briefcase←
5: GSAR
CENTRAL isave L in the local file system of site S
11: NODES
12: NODES NODES
13: NODES
14: if NODES
15: AObject newGKDA(AgentProfile)←
17: NODES
18: else
23: end if
24: end procedure

92
Figure 2. Control Panel of AeMGSAR
4.IMPLEMENTATION AND PERFORMANCE STUDY
All the agents as well as control panel as shown in Figure 2 are designed in Java. Synthetic
dataset ( iDB ) is stored across three distributed sites 1S , 2S and 3S , with 3500, 3850 and 3900
transactions and 10 items in each respectively using Transactional Data Set Generator (TDSG)
tool [16]. Binary and transactional versions of these datasets are shown in Appendix A. The
required configuration of the system is shown in Table 1 with additional deployment of DM_AEE
at each distributed site and AL and RM at CENTRALS . Round Trip time taken by various MAs is
shown in Figure 3. CPU time consumed by various MAs at site 1S , 2S and 3S is shown in Figure
4, Figure 5 and Figure 6, respectively. CPU time for GFIGA and GKGA is 101357102 nano
seconds and 33317458 nano seconds, respectively. ( )
FI
k iL and ( )
FISC
k iL at distributed sites generated by
LFIGA agent with 20% min_th_sup are shown in Appendix B.1, B.2 and B.3. LSAR
iL at distributed
sites generated by LKGA agent with 50% min_th_conf are shown in Appendix B.4, B.5 and B.6.
Globally frequent itemsets generated by GFIGA at CENTRALS is shown in Figure 7. Fifteen numbers
of 2-itemsets and eight number of 3-itemsets are globally frequent in TFI
kL list and 4, 5 and 6-
itemsets, which are locally frequent, are not globally frequent. Globally strong association rules
( GSAR
CENTRALL ) generated by GKGA at CENTRALS for globally frequent 3-itemsets are shown in Figure 8
and GSAR
CENTRALL for 2-itemsets are shown in Appendix B.7.
On comparing this system with the traditional central data warehouse (DW) based approach for
ARM where entire data from the distributed sites is centrally collected in a DW [17], it is found
that the storage cost is reduced as data is mined locally and only the resultant knowledge is
carried at the central site by mobile agents. As size of the resultant data carried across by mobile

93
agents is small so network communication cost is also reduced in this case. Data mining is
performed locally by agents, so computational cost at central site is also minimised. AeMGSAR
reflects the global knowledge because all the strong association rules generated are also strong at
each distributed site. The system relies upon the Java's in-built security system. As MAs are
scalable in nature so performance would not be affected by adding more sites.
Table 1. Network Configuration
Site Name Processor OS
LAN Configuration
IP a
Network
SCENTRAL Intel b
MS c
192.168.46.5 NW d
S1 Intel b
MS c
192.168.46.212 NW d
S2 Intel b
MS c
192.168.46.189 NW d
S3 Intel b
MS c
192.168.46.213 NW d
a. IP address with Mask: 255.255.255.0 and Gateway 192.168.46.1
b. Intel Pentium Dual Core(3.40 GHz, 3.40 GHz) with 512 MB RAM
c. Microsoft Windows XP Professional ver. 2002
d. Network Speed: 100 Mbps and Network Adaptor: 82566DM-2 Gigabit NIC
Figure 3. Round Trip time taken by various MAs
Figure 4. CPU Time taken by various MAs at site 1S

94
Figure 7. Lists of global frequent k-itemsets at CENTRALS

95
Figure 8. Globally strong association rules for globally frequent 3-itemsets
5.CONCLUSION
Mobile agents strongly qualify for designing distributed applications and the amalgamation of
DDM and agent technology gives favourable results. Most of the existing agent based
frameworks for DARM task are only prototype model and lacks the appropriate underlying
execution environment, scalability, privacy preserving techniques, global knowledge generation
and implementation using a real datasets. In this study, a scalable MAS, called Agent enabled
Mining of Globally Strong Association Rules (AeMGSAR), is presented based on the serial
itinerary of the mobile agents. In this system the overall task of mining the globally strong
association rules is divided into subtasks which are handled by various mobile as well as
stationary agents. An AEE is also designed for the implementation and performance study of
AeMGSAR system. Serial itinerary used for mobile agent migration increases the overall cost of
DARM task so a parallel computing model could be designed where clones of each mobile agent
is dispatched in parallel to all distributed sites.
REFERENCES
[1] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth & R. Uthurusamy, (1996) Advances in Knowledge
Discovery and Data Mining, AAAI/MIT Press.
[2] J. Han & M. Kamber, (2006) Data Mining: Concepts and Techniques, 2nd ed. Morgan Kaufmann.
[3] G. S. Bhamra, R. B. Patel & A. K. Verma, (2014) “Intelligent Software Agent Technology: An
Overview”, International Journal of Computer Applications (IJCA), vol. 89, no. 2, pp. 19–31.
[4] R. Agrawal, T. Imielinski & A. Swami, (1993) “Mining association rules between sets of items in large
databases”, in Proceedings of the ACM-SIGMOD International Conference of Management of Data,
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[5] R. Agrawal & J. C. Shafer, (1996) “Parallel mining of association rules”, IEEE Transaction on
Knowledge and Data Engineering, vol. 8, no. 6, pp. 962–969.
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no. 4, pp. 14–25.
[7] X. Wu & S. Zhang, (2003) “Synthesizing high-frequency rules from different data sources”, IEEE
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[8] Y.-L. Wang, Z.-Z. Li & H.-P. Zhu, (2003) “Mobile agent based distributed and incremental techniques
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Distributed Data Mining”, in Proceedings of the International Conference on Computational
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[12] G. Hu & S. Ding, (2009b) “Mining of Association Rules from Distributed Data using Mobile
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[14] G. S. Bhamra, A. K. Verma, & R. B. Patel, (2015) “Agent Based Frameworks for Distributed
Association Rule Mining: An Analysis”, International Journal in Foundations of Computer Science &
Technology (IJFCST), vol. 5, no. 1, pp. 11-22.
[15] R. Agrawal & R. Srikant, (1994) “Fast Algorithms for Mining Association Rules in Large Databases”,
in Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94). Morgan
Kaufmann Publishers Inc., pp. 487–499.
[16] G. S. Bhamra, A. K. Verma, & R. B. Patel, (2011) “TDSGenerator: A Tool for generating synthetic
Transactional Datasets for Association Rules Mining”, International Journal of Computer Science
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[17] G. S. Bhamra, A. K. Verma, & R. B. Patel, (2014) “An Investigation into the Central Data Warehouse
based Association Rule Mining”, International Journal of Computer Applications (IJCA), vol. 96, no.
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AUTHORS
Gurpreet Singh Bhamra is currently working as Assistant Professor at
Department of Computer Science and Engineering, M. M. University, Mullana,
Haryana. He received his B.Sc. (Computer Sc.) and MCA from Kurukshetra
University, Kurukshetra in 1995 and 1998, respectively. He is pursuing Ph.D.
from Department of Computer Science and Engineering, Thapar University,
Patiala, Punjab. He is in teaching since 1998. He h as published 13 research
papers in International/National Journals and International Conferences. He has
received Best Paper Award for “An Agent enriched Distributed Data Mining on
Heterogeneous Networks”, in “Challenges & Opportunities in Information
Technology” (COIT-2008). He is a Life Member of Computer Society of India. His research interests are in
Distributed Computing, Distributed Data Mining, Mobile Agents and Bio-informatics.
Dr. Anil Kumar Verma is currently working as Associate Professor at
Department of Computer Science & Engineering, Thapar University, Patiala. He
received his B.S., M.S. and Ph.D. in 1991, 2001 and 2008 respectively, majoring in
Computer science and engineering. He has worked as Lecturer at M.M.M.
Engineering College, Gorakhpur from 1991 to 1996. He joined Thapar Institute of
Engineering & Technology in 1996 as a Systems Analyst in the Computer Centre
and is presently associated with the same Institute. He has been a visiting faculty to
many institutions. He has published over 100 papers in referred journals and
conferences (India and Abroad). He is a MISCI (Turkey), LMCSI (Mumbai),
GMAIMA (New Delhi). He is a certified software quality auditor by MoCIT,

97
Govt. of India. His research interests include wireless networks, routing algorithms and securing ad hoc
networks and data mining.
Dr. Ram Bahadur Patel is currently working as Professor and Head at Department
of Computer Science & Engineering, Chandigarh College of Engineering &
Technology, Chandigarh. He received PhD from IIT Roorkee in Computer Science &
Engineering, PDF from Highest Institute of Education, Science & Technology
(HIEST), Athens, Greece, MS (Software Systems) from BITS Pilani and B. E. in
Computer Engineering from M. M. M. Engineering College, Gorakhpur, UP. Dr.
Patel is in teaching and research since 1991. He has supervised 36 M. Tech, 7 M.
Phil. and 8 PhD Thesis. He is currently supervising 6 PhD students. He has published
130 research papers in International/National Journals and Refereed International
Conferences. He has written 7 text books for engineering courses. He is member of
ISTE (New Delhi), IEEE (USA). He is a member of various International Technical Committees and
participating frequently in International Technical Committees in India and abroad. His current research
interests are in Mobile & Distributed Computing, Mobile Agent Security and Fault Tolerance and Sensor
Network.
APPENDIX A – SYNTHETIC DATASETS
A.1 BDS3500T10I.txt and corresponding TDS3500T10I.txt( 1DB ) at site 1S
These synthetic binary and transactional datasets of 3500 records are created by TDSG tool at
site 1S . In the binary version each column head represents the item number and each row
represents a transaction where integer ‘1’ is used for a purchased item and ‘0’ is used if it is nor
purchased. The corresponding transactional version has a Transaction It (TID) for each
transaction and Itemset is the set of all the purchased items for that particular transaction.

98
These synthetic binary and transactional datasets of 3850 records are created by TDSG tool at site
2S .
These synthetic binary and transactional datasets of 3900 records are created by TDSG tool at site
3S .

99
APPENDIX B–RESULTANT KNOWLEDGE OF AEMGSAR
SYSTEM
B.1 (1)
FI
kL and (1)
FISC
kL at site 1S
List of frequent k-itemset, i.e., (1)
FI
kL is represented by column L and column SC shows the support
count of the corresponding frequent k-itemset, i.e., (1)
FISC
kL at site 1S . These frequent itemsets and
their support counts are obtained by processing the synthetic dataset ( 1DB ) as shown in Appendix
A.1.
B.2 (2)
FI
kL and (2)
FISC
kL at site 2S
These frequent itemsets and their support counts are obtained by processing the synthetic dataset
( 2DB ) as shown in Appendix A.2.

100
B.3 (3)
FI
kL and (3)
FISC
kL at site 3S
These frequent itemsets and their support counts are obtained by processing the synthetic dataset
( 3DB ) as shown in Appendix A.3.

101
B.4 1
LSAR
L at site 1S
Column L represents frequent k-itemset and column AR(support, confidence) shows the list of
locally strong association rules, i.e., 1
LSAR
L at site 1S . Each strong rule has its associated support
and confidence factor. The minimum threshold is taken as 20% and minimum threshold
confidence as 50% for generating the strong rules by making use of the data as shown in
Appendix B.1.

102
B.5 2
LSAR
L at site 2S
LSAR
Appendix B.2.

103
B.6 3
LSAR
L at site 3S
LSAR
Appendix B.3.

104
B.7 GSAR
CENTRALL at site CENTRALS
Column L represents globally frequent k-itemset, i.e., itemsets which are locally strong at all the
distributed sites and column AR(support, confidence) shows the list of globally strong
association rules, i.e., GSAR
CENTRALL for such itemsets. Each globally strong rule has its associated
support and confidence factor. The minimum threshold is taken as 20% and minimum threshold
confidence as 50%. Site represents the IP address of the site where the rule is locally strong. IP
address 192.168.46.212 is used for site 1S , 192.168.46.189 for site 2S and address
192.168.46.213 is used for site 3S .

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