DATA MINING SEMINAR

TOPIC: DATAMINING FOR

TELECOMMUNICATIONS

SUBMITTED BY: MAYURI KIRAN

ANVEKAR

SUBMITTED TO: RINO CHERIAN

DATE: 30/03/2012

Table of Contents I. ABSTRACT .............................................................................................................................................. 3

II. INTRODUCTION ..................................................................................................................................... 3

III. TYPES OF TELECOM DATA ................................................................................................................. 4

1. Call Summary Data ............................................................................................................................ 4

2. Network Data .................................................................................................................................... 4

3. Customer Data .................................................................................................................................. 4

IV. Mining Sequential Patterns In Telecommunication Database Using Genetic Algorithm ................. 5

1. Sequential Patterns Mining ............................................................................................................... 5

2. Genetic Algorithm ............................................................................................................................. 5

3. Mining Sequential Patterns in Telecommunication Database Using GA .......................................... 6

3.1 Chromosome. .................................................................................................................................. 6

3.2 Genetic Operators ........................................................................................................................... 7

3.3 Fitness Function. ............................................................................................................................. 7

3.4 SPT-GA Algorithm ............................................................................................................................ 8

4. Experiment Results ........................................................................................................................... 9

V. Discovering Structural Patterns In Telecommunications Data ........................................................... 11

VI. CONCLUSION ................................................................................................................................... 13

VII. REFERENCES .................................................................................................................................... 13

I. ABSTRACT

Telecommunication companies generate a tremendous amount of data. These data include call

detail data, which describes the calls that traverse the telecommunication networks, network

data, which describes the state of the hardware and software components in the network, and

customer data, which describes the telecommunication customers. This chapter describes how

data mining can be used to uncover useful information buried within these data sets. Several data

mining applications are described and together they demonstrate that data mining can be used to

identify telecommunication fraud, improve marketing effectiveness, and identify network faults.

II. INTRODUCTION

The international telecommunications play an important role in message sharing and information

passing. In the last decade a dramatic change in the structure of telecommunications companies

has been taken place, from public monopolies to private companies. The quick development of

mobile telephone networks and video calling and Internet technologies has created enormous

competitive pressure on the companies sector. As new competitors arise in market, telecom need

intelligent tools to gain profit and withstand. Also, stock market expectations are huge and

investors, financial analysts need tested tools to gain information about how companies perform

financially compared to their competitors, what they are good at, who are the major competitors

are, etc. In other term, the telecom companies need to benchmark their performances against

compete trends in order to remain important role in this market. There is an enormous amount of

information about these companies financial performance that is now publicly available. This

amount greatly exceeds our capacity to analyze it; the problem is that we often lack tools to

quickly and accurately process these data.

Data mining for telecommunications companies involve the use of simple, traditional and

advanced mathematical techniques used to analyze large populations of data and deliver insights,

forecasts, explanations and predictions of how systems, customers, network and marketplace are

likely to react to different situations. The hypercompetitive nature of the telecom industry has

created a need to understand customers, to keep them, and to model effective ways to market

new products. This creates a great demand for innovation like data mining technique to help

understand the new business trends involved, catch fraudulent activities, identify

telecommunication patterns, make better use of resources and improve the quality of services.

III. TYPES OF TELECOM DATA

The Initial step in the data mining process is to understand the data. Here we discuss three main

types of telecom data. They are as follows:

1. Call Summary Data

Every time a call is placed on a telecom network, descriptive information about the call is saved

as a call detail for future record. At a minimum, each call detail record will include the

originating and terminating phone numbers, the date and time of the call and the duration of the

call.

2. Network Data

Telecommunication networks are extremely complex configurations of equipment, comprised of

interconnected components. Each network element is capable of generating error and status

messages, which leads to a tremendous amount of network data.

3. Customer Data

Telecommunication companies, like other businesses have millions of customer’s. For necessity

they have to maintaining a database of information on these customers. This information will

include name, address and may include other information such as service plan, credit score,

contract information, family income and payment history.

IV. Mining Sequential Patterns in Telecommunication Database

Using Genetic Algorithm

Sequential pattern mining is the process of finding the relationships between occurrences of

sequential events, to find if there exists any specific order of the occurrences. The extraction of

sequential pattern is not polynomial in time of execution. The other algorithms for performing

sequential pattern mining can assure optimum solutions but they do not take into consideration

the time taken to reach such solutions. Whereas this algorithm based on genetic concepts gives a

non-optimal solution but in a reasonable time (polynomial) of execution.

1. Sequential Patterns Mining

The goal is to find all sub sequences from the given sets of transactions; this approach is useful

when the data to be mined have some sequential nature to deal with databases that have a time-

series characteristics. Sequential Pattern can be defined as follows.

Definition: Let I ={x1...xn} be a set of items. An itemset is a non-empty subset of items, and an

itemset with k items is called k-itemset. A sequence s=(X1...Xl) is an order list of item sets, and

an item set Xi (1≤ i ≤ l) in a sequence is called a transaction. In a set of sequences, a sequence s

is maximal if s is not contained in other sequences.

2. Genetic Algorithm

Genetic Algorithm (GA) is a part of evolutionary computing, which is a rapidly growing area of

artificial intelligence. Genetic algorithm starts with a set of solutions (represented by

chromosomes) called population. Solutions from one population are taken and used to form a

new population by mutation and crossover. This is motivated by a hope, that the new population

will be better than the old one.

Best solutions which are selected to form new solutions (offspring) are selected according to

their best fitness. This is repeated until some condition (for example number of populations or

improvement of the best solution) is satisfied. To measure the quality of a solution, fitness

function is assigned to each chromosome in the population.

3. Mining Sequential Patterns in Telecommunication Database Using

Genetic Algorithm

Database, this algorithm is called SPT-GA algorithm. Firstly, we present our chromosome

structure and encoding schema, genetic operators, and then we define the fitness assignment and

selection criteria. Finally, we give the structure of SPT-GA algorithm.

3.1 Chromosome. The used structure of GA chromosomes and how it is represented is as

follows:

3.1.1 Structure. In Telecommunication Database, country code values are used for creating the

chromosomes.

In the algorithm, chromosomes have a fixed length, and its length is equal to number of country

codes that are available in the database as in Figure 1.

3.1.2 Representation. In Genetic Algorithm, there are many alternatives to represent a

chromosome based on other problem like binary and integer representation. To decide which

representation is better to be used for Sequential Pattern rules, we should use the short; low-order

schemata are relevant to the underlying problem and relatively unrelated to schemata over other

fixed positions. Also we should select the smallest alphabet that permits a natural expression of

the problem, presented in [8].

In SPT-GA algorithm, we choose the binary representation because it is the most suitable for

our algorithm and it needs less space and it represents the needed information (element occurred

or not).

For example, using the Telecommunication Database in Table.1, if a sequence is equal to <249,

973, 91>, it can be represented as in Figure.2.

Additionally, as you can see in Figure.2, order cannot be extracted directly. To solve this

problem, we decided to associate the transactions sequence as a metadata with each

chromosome. For that, we use Vertical Bitmap Representation that makes SPT-GA algorithm to

take less time and space to be executed.

3.2 Genetic Operators. SPT-GA uses genetic operators to generate the offspring of the existing

population.

Genetic algorithm will send chromosomes that represented by binary string where each bit

corresponds to an element occurrence (0 or 1); the number of bits is equal to the number of

items. After encoding of the solution domain, initially many chromosome solutions are randomly

generated depending on population size.

Genetic algorithm will select the chromosome regarding to our fitness function. The major

measure that used by our algorithm is Sequential Interestingness measure (SIM).

Then crossover takes place, it selects genes from parent chromosomes and creates a new

offspring. The simplest way how to do this is to choose randomly some crossover point and

everything before this point copy from a first parent and then everything after a crossover point

copy from the second parent. Crossover is shown in Figure 3.

After a crossover is performed, mutation takes place. This is to prevent falling all solutions in

population into a local optimum of solved problem. Mutation changes randomly the new

offspring. For binary encoding we can switch a few randomly chosen bits from 1 to 0 or from 0

to 1. Mutation is shown in Figure 3.

3.3 Fitness Function. Fitness function method discovers sequential patterns corresponding to

the interests of users without using background knowledge. This method defines a new criterion

called the sequential interestingness measure (SIM).

Definition: The sequential interestingness measure of a rule AC is:

SIM(AC) = min Ci ∈ C {(Confidence(A|Ci))α} × Support(AC)

Where: (α ≥ 0): is a confidence priority that represents how important the frequency of the

pattern is

Ci: is subsequence of C, it represents a condition of sequence C

i = 1 … n where n is the number of conditions in C.

The first term of the criterion evaluates that the frequencies of the sub-patterns are not frequent

while the second term evaluates that the frequency of the pattern is frequent.

3.4 SPT-GA Algorithm. The SPT-GA algorithm that was proposed is described in this section.

In Figure 4, the pseudo code of SPT-GA algorithm is presented.

ALGORITHM

Input:

Initial samples size: N

Maximum generations: G

Threshold value: T

Minimum fitness: minF

Output:

Malpractice user no lists

Begin

Step1: Initialize counter count = 0

Step2: Initial population IN of size N

Step3: For each chromosome i ∈ IN

If S1& S2 is given then

Measure the fitness F(i, S1, S2,T)

Else If S1 is given then

Measure the fitness F(i, S1, T)

Else If S2 is given then

Measure the fitness F(i, S2, T)

Else

Measure the fitness F(i, T)

Step4: Mutate and crossover P.

Step5: IF (fitness ≥ minF)

Select fittest rules from P

Step6: Set temp = temp +1

Step7: IF (t > G) then

S = P

Stop

Else

Go to Step 3

End

After the encoding of the dataset, using bitmap representation, the algorithm starts by selecting

individuals to initial population. Then the following processes are repeated until the pre-specified

maximum number of generations is achieved. The fitness values determined for each selected

individual given the rule antecedent or consequent. The fittest rules that are larger than or equal

minimum fitness in P will be selected. Giving the antecedent or consequent is not mandatory but

it will reduce the time of search and extract more desired rules.

Existing chromosomes are used in generating new ones by applying crossover and mutation

operators. Chromosomes survive based on their fitness used in the process. This way, the

interesting set is determined and the target is achieved.

4. Experiment Results

The results of some experiments on telecommunication database is presented and analyzed as

follows. SPT-GA algorithm is written with MATLAB programming language. The user can tune

population size (N), generations (G), confidence priority (α) and minimum fitness (minF).

A Telecommunication Database is taken from a Telecommunication Company and it has 1091

transactions and 60 country codes. The crossover probability used is 0.8, while the mutation is

0.001. The output of this experiment is a text file that includes the interesting rules that represent

the most suitable telecommunication sequences. For example, the rule of Figure 5 told us that

when country code 91 is called, that means (40%) of callers will call country code 92 afterwards.

In SPT-GA algorithm, there are four parameters that must be determined by a user: number of

generation (G), population size (N), confidence priority (α) and minimum fitness (minF). First

experiment sets N = 20, α = 1 and minF = 0 with G = [20...200]. The second experiment sets G =

20, α = 1 and minF = 0 with N = [20...200]. The two experiments were done on two days of calls

with 1091 transactions and 60 country codes.

According to the experiment, Figure 6 shows the time, in seconds, spent by the SPT-GA

algorithm to extract the best rules while Figure 7 shows the average fitness of the final output.

Both figures shows the result related to increasing the generations and population size.

The tests showed that when the generation increases the time will be around each other but

when the population increases the time will be increased. However, comparing the two

experiments, as shown in Figure 6, GA takes less time when increasing the generation than

increasing the population. Moreover, the tests also showed that when the generation and

population increase, the best fitness will be around.

From the experiments, it is observed that increasing of generation will take less time than

increasing of population size. But, either ways, GA does not take a long time; it is only a matter

of seconds. In addition, increasing the generation and the population will not guarantee a large

improvement of average fitness.

V. Discovering Structural Patterns In Telecommunications

Data

The SUBDUE system is a structural discovery tool that finds substructures in a graph-based

representation of structural databases using the minimum description length (MDL) principle.

SUBDUE discovers substructures that compress the original data and represent structural

concepts in the data. Once a substructure is discovered, the substructure is used to simplify

the data by replacing instances of the substructure with a pointer to the newly discovered

substructure. The discovered substructures allow abstraction over detailed structures in the

original data. Iteration of the substructure discovery and replacement process constructs a

hierarchical description of the structural data in terms of the discovered substructures. This

hierarchy provides varying levels of interpretation that can be accessed based on the specific

goals of the data analysis.

SUBDUE represents structural data as a labeled graph. Objects in the data map to vertices or

small sub graphs in the graph, and relationships between objects map to directed or undirected

edges in the graph. A substructure is a connected sub graph within the graphical representation.

This graphical representation serves as input to the substructure discovery system. Figure 8

shows a geometric example of such an input graph. The objects in the figure become labeled

vertices in the graph, and the relationships become labeled edges in the graph. The graphical

representation of the substructure discovered by SUBDUE from this data is also shown in Figure

8. One of the four instances of the substructure is highlighted in the input graph. An instance of a

substructure in an input graph is a set of vertices and edges from the input graph that match,

graph theoretically, to the graphical representation of the substructure.

Figure 8: Example Substructure in Graph Form

Experiment Results:

In a study upon running the SUBDUE system on the data in graph form, the six substructures

output by the system were analyzed. Figure 9 shows two of these six substructures. The

substructures in this figure show that the following patterns are common:

1. Local calls originating between 11:00am and 2:30pm with 30 to 45 minute durations and

2. Long distance calls originating between 6:00pm and 10:00pm with 5 to 20 minute durations.

Figure 9: Telecom pattern discovered by Subdue.

VI. CONCLUSION

Genetic Algorithm is applied to find frequent sequences in Telecommunication Database in order

to help Telecommunication companies to know the country codes that have a relation between

them. So, the telecommunication companies can estimate the countries that have a specific order

of the occurrences and give a discount on the calls to these countries. SPT-GA algorithm utilizes

the property of evolutionary algorithm that discovers best rules in a short time with meaningful

results.

VII. REFERENCES

1. Data Mining In Telecommunications; Gary M. Weiss

2. Data Mining In Telecommunications And Studying Its Status In Iran Telecom

Companies And Operator; Jamal Sophieh

3. Data Mining And CRM In Telecommunications; D. Camilovic

4. Genetic Algorithms; William H. Hsu

5. A Fraud Detection Approach in Telecommunication using Cluster GA; V.Umayaparvathi

& Dr.K.Iyakutti