Ijcot v5p312

International Journal of Computer & Organization Trends – Volume 5 – February 2014

Activity Based Data Management in Mobile Environment Using Data Mining Techniques Satwant Kaur#1, Er.Rishma Chawla*2, Er.Varinderjit Kaur@3 1

M. Tech, Department of CSE, RIET, Phagwara, Punjab, India HOD, Department of CSE, RIET, Phagwara, Punjab, India 3 AP, Department of CSE, RIET, Phagwara, Punjab, India 2

Abstract - Wireless communications and mobile device

important rule to manage the database. [2]Data mining is an

technologies

wireless

emerging research area, whose goal is to discover potentially

communications applications but also enable the provision of

useful information in database. Hence the data mining has

plentiful

not

kinds

only

of

mobile

accentuate

services

various

.In

mobile

service

environments, mobile users may request various kinds of services and applications through mobile devices or laptop computers from arbitrary locations at any time via 3G, WiMax,

attracted a great deal of attention in recent years. [3]There are several data mining techniques are available to analyze of data. Decision tree rule is one most important technique of

wireless LAN, or other wireless networking technologies. Most

data mining. Based on decision tree, implement the three

users in a mobile environment are moving and accessing

algorithms ID3, CART, Enhanced algorithm on the activity

wireless services for the activities they are currently engaged in.

based data in mobile environment. data mining algorithm

It is propose the idea of complex activity for characterizing the

which involves incremental mining for user moving patterns

continuously changing complex behavior patterns of mobile

in a mobile computing environment and exploit the mining

users. An activity may be composed of sub activities. For the

results to develop data allocation schemes so as to improve

purpose of data management, a complex activity is modeled as a sequence of location movement, service requests, the cooccurrence of location and service. So, therefore, purpose new method for complex activity to manage database. In particular

the overall performance of a mobile system, reduce the time of execution and accuracy rate. In this paper describe the data mining, ID3, CART, and Enhanced algorithm .Implement

devise ID3, CART and new invent algorithm Enhanced

this algorithm on the WEKA tool and comparing the different

algorithm. Preliminary implementation and simulated results

parameters of algorithm.

that the proposed framework and techniques can significantly

II. DATA MINING

reduce the response time increase the performance, increase accuracy rate. Implement these algorithms on WEKA tools.

Compare these algorithms with different parameters.

It is knowledge discovery, the computer-assisted process of digging through and analyzing enormous sets of data and

Keywords- Data Mining, ID3 Algorithm, CART Algorithm, Enhanced Algorithm.

then extracting the meaning of the data. [4] Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different

I. INTRODUCTION

dimensions or angles, categorize it, and summarize the Rapid advancement in mobile user environment is important

relationships identified. Data mining consists of five major

to manage the data of mobile users [1]. Data mining play

elements:

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International Journal of Computer & Organization Trends – Volume 5 – February 2014 

IV. CART ALGORITHM

Extract, transform, and load transaction data onto the data warehouse system.

CART [7] is an acronym for classification and regression trees, a decision –tree procedure introduce in 1984 by world



Store and manage the data in a multidimensional database system.



renowned UC Berkeley and Stanford, Richard olshen and Charles stone [8].the CART methodology solves a numbers

Provide data access to business analysts and

performance, accuracy, and operational problems that still plague

information technology professionals.

many

current

decision-tree

methods.

CART

innovations include: 

- solving the “how big to grow the tree

Analyze the data by application software.

“problems

- using strictly two-way splitting 

Present the data in a useful format, such as a graph

- incorporating automatic testing and tree validation,

or table.

-Providing a completely new method for handling missing values.

III. ID3 ALGORITHM

Features of CART Algorithm

[5] ID3 algorithm is the decision tree classification method based on information entropy, which selects the type of cases according to the attribute set values and regards the information entropy as it target evaluation function , using top-down non-return strategy to search a part of all the space to ensure that build decision tree is the most simple and each test datum is the least.[6]ID3 algorithm uses information entropy theory to select attribute values with maximum information gain in the current sample sets as the test attribute. Whereas the division of the samples sets is based on the value of the test properties, the numbers of test attributes decide the number of sub-sample sets, at the same time, new leaf nodes grow out of corresponding nodes of the sample set on the decision tree. Since the more simple the decision tree structure is, the easier for one to summarize the law of things in nature, the average paths from the non-leaf nodes to the descendant nodes are expected the shortest. The average depth of the decision tree is minimum, which requires choosing a good division on each node.

1. The visual display enables users to see the hierarchical interaction of the variables; 2. Future, because simple if then rules can be read right off the tree, models are easy to apply to new data.

3. CART uses strictly binary, or two-way, splits that divide each parent node into exactly two child nodes by posting question with yes/no answers at each decision node.

4. CART is unique among decision-tree tools. CART – proven methodology is characterized by [9]: (a) Reliable pruning strategy- CART developers determined definitively that (b) No stopping rule could be relied on to discover the optimal tree, (c) Powerful binary-split search approach-CART binary decision tree are more sparing with data is left for learning. (d) Automatic self-validation procedures- In the search for patterns in database it is essential to avoid the trap of over fitting. (e) Future, the testing and selection of the optimal tree are an integral part of the CART algorithm. (f) It has automated solutions that surrogate splitters intelligently handle missing values;

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International Journal of Computer & Organization Trends – Volume 5 – February 2014 Table 1: Attributes of dataset

(g) Multiple–tree, committee-of-experts methods increase the precision of results.

Sr.no

Attributes

V. ENHANCED ALGORITHM

1.

SSID

The tree starts as a single node representing the training

2.

Strength

samples. If the samples are all of the same class, then the

3.

Authenticate type

4.

Frequency

5.

Channel

will best separate the samples into individual classes. This

6.

Speed

attribute becomes the “test” or “decision” attribute at the

7.

Bandwidth

node. (All of the attributes are categorical or discrete value.

8.

Transmitted frame count

9.

service

node becomes a leaf and is labeled with that class. Otherwise, the algorithm uses an entropy-based measure known as information gain as a heuristic for selecting the attribute that

Continues-valued attribute must be discretized.)

A branch is created for each known value of the test attribute, and the samples are partitioned accordingly. The algorithm uses the same process recursively to form a decision tree for the samples at each partition. Once an attribute has occurred at a node, it need not be considered in any of the node’s Descendents.

The recursive partitioning stops only when any one of the following conditions is true: All the samples for a given node belong to the same class or there are no remaining attributes on which the samples may be further partitioned. In this case, majority voting is employed. This involves converting the given node into a leaf and labeling it with the class in majority among samples.

Alternatively, the class distribution of the node samples may be stored. There are no samples for the branch test-attribute.

In this case, a leaf is created with the majority class in samples.

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International Journal of Computer & Organization Trends – Volume 5 – February 2014 VI. SIMULATION RESULT

1.3 IMPLEMENTATION OF THE CART ALGORITHM ON WEKA

Table 2: Collected Dataset

Figure 2: CART Algorithm Implementation on Weka

1. IMPLEMENTATION OF ID3, CART AND ENHANCED ALGORITHM

1.4 IMPLEMENTATION OF THE ID3 ALGORITHM

1.2 IMPLEMENTATION OF THE ENHANCED

ON WEKA

ALGORITHM ON WEKA

Figure 1: Enhanced Algorithm Is Implementation on Weka

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Figure 3: ID3 Algorithm on Weka

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International Journal of Computer & Organization Trends – Volume 5 – February 2014 Compare the ID3, CART and Enhanced Algorithm

Correctly Classified Instances

with Different Parameters Table 3: Compare the ID3, CART and ENHANCED Algorithm Different Parameters

ID3

CART

EA

Execution Time

0.11

0.13

0.02

Correctly

87.2%

51.6%

34%

4.6%

48.3%

65.3%

37.3%

94.3%

96.3%

classified instance Incorrectly classified instance

100 90 80 70 60 50 40 30 20 10 0

Correctly Classified Instances

ID3

Accuracy

CART

EA

Figure 5: Show the Analysis Between Of CCI

Incorrectly Classified Instances

Execution Time

70 60

EA

50

CART

Execution Time

40

Incorrectly Classified Instances

30

ID3

20

0

0.05

0.1

0.15 10

Figure 4: Analysis Between of Execution Time

0 ID3

CART

EA

Figure 6: Analysis Between of Incorrect Classified Instance

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International Journal of Computer & Organization Trends – Volume 5 – February 2014 [4] Margaret H.Dunham,”Data Mining Introductory and

120

Advanced

100

topic”,published

by

person

education

Delhi,India,[2004].

80

[5] Quinlan J R,”Induction of decision tree,”Machine

60

Accuracy

Learning, vol.4,no.2,pp.81-106,[1986].

40

[6] Ren Yanna, “ The Design of Algorithm for Data

20

Mining System Used for Web Service” ,IEEE [2011] .

[7] G.Sathyadevi “application of CART algorithm hepatitis

0 ID3

CART

disease

EA

diagnosis”,IEEE-International

Conference

on

recent trends in information technology,ICRTIT 2011,June 3-5,[2011].

Figure 7: Analysis between Accuracy in Data

[8] K. Cios, W.Pedrycz, and R. Swiniarsski. Data Mining VII. CONCLUSIONS

Methods

for

Knowledge

Discovery.Boston:Kluwer

In this Research, It is highlight the approaches for creating

Academic Publishers,[1998]

a decision tree. They are mainly available into academic

[9] Cheeseman, P., and Stutz, J. 'Bayesian Classification

tools from the machine learning community. This is note

(AUTOCLASS): Theory and Results In Advancesin

that they are an alternative quite credible to decision trees

Knowledge Discovery and Data Mining, eds. PP. 51

and classification rules, both in terms of accuracy than in

Articles FALL.[1996 ].

terms of processing time. After analysis Order ID3, CART and Enhanced algorithm is more suitable to find accurate and consuming less access time to mine data with minimum error rate. So Enhanced Algorithm is a best algorithm for mining a data on mobile environment data set.

REFERENCES [1] W. Ma, Y. Fang, and P. Lin, “Mobility Management Strategy Based on User Mobility Patterns in Wireless Networks,” IEEE Trans. Vehicular Technology, vol. 56, no. 1, pp. 322-330, Jan. [2007] [2] B.N.Lakshmi,G.H Raghunandhan “A conceptual overview of data mining”,IEEE ,Proceeding of the national conference on innovation in emerging technology,pp.2732,17&18 feb,[2011].

[3] Omer Adel Nassar,Dr.Nedhal A.Saiyd,”the integrating between web usage mining and data mining techniques,”5th internal conference on computer science and information technology,[2013]

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