ICMIS-2010 1 Knowledge Management Approach for Predictive ...

ICMIS-2010 1

Knowledge Management Approach for Predictive Analytics in

Marketing DSS using Temporal Data Mining techniques

Sunita Soni, Jyothi Pillai, Department of Computer Applications, Bhilai Institute of Technology,Durg

Dr. Ranjana Vyas,MATS University,Raipur

Dr. O.P.Vyas Indian Institute of Information Technology Allahabad.

International Conference on Information Security & Management of Technologies in Business

ICMIS-2010 2

Overview

Knowledge Management Approach for Predictive Analytics in Marketing DSS using Temporal Data Mining techniques

Introduction

Predictive Analysis in Marketing DSS

Temporal Associative Classifier

Knowledge Management Frame work

Experimental Results

Conclusions and Future Scope

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Introduction

Knowledge Discovery in Databases.

Data Mining

Predictive Analytics


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Knowledge Discovery in Databases-KDD

KDD is non-trivial extraction of implicit, previously unknown and potentially useful knowledge from data.

KDD is a significant concept related to data mining and business intelligence.


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Introduction

Knowledge Discovery in Databases

Data Mining



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Data Mining Data mining: the core of KDD.

Data Mining: discovery of hidden knowledge, unexpected patterns and new

rules from large databases.

KDD describes the whole process of extraction whereas Data mining is used exclusively for the discovery stage of KDD process.


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Data Mining in various forms is becoming a major component of business operations.

Business processes involving data mining for Knowledge Management–

Customer Relationship Management, Business Intelligence,

Supply Chain Optimization, Demand Forecasting, Assortment Optimization, etc.

Data Mining in Business Processes


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Introduction

Knowledge Discovery in Databases

Data Mining



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Data Mining Tasks

• Common data mining tasks Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Regression [Predictive] Deviation Detection [Predictive]

Prediction Tasks -Predict unknown or future values of variables

Description Tasks -Find human-interpretable patterns from data.


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Overview


Introduction






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Marketing DSS


“Marketing is the process of Identification of Demand and fulfilling it in a profitable manner and in case of no demand or less demand, it is the process of creation of demand and then fulfilling it in a profitable manner.”

Types of Decision in Marketing (4 P’s).

1. Product: Modify the product in terms of size, quality, quantity etc.

2. Price :Cash, EMI, Discount policy, interest rate in case of EMI.

3. Promotion: Advertising policy .

4. Place : Direct to the customer, Channel, Through retailer

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To take any decision regarding four piece of marketing ie Product/Price/Promotion/place we have to analyze the environment factors, competitors, consumer, past experience and performance.

Today , huge data repository are being maintained in every field including business and valuable bits of information are embedded in these data repository.

Because of huge size of the data source makes it impossible for a human analyst to come up with interesting information(or pattern) that will help in the Decision Making Process.



the Solution is Data Mining

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Data mining is related to building Decision Support Systems.

First, data mining can help identify relations and rules that can be incorporated in Knowledge-driven DSS.

Second, case-based reasoning can be used to create a specific Knowledge-driven DSS that can be used by a manager or a knowledge worker who is trying to diagnosis problems in that "case" environment.

Third, data visualization tools can be incorporated with a structured data set to assist managers in making a recurring decision where the data set is routinely updated.



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Direct Marketing– Goal: Reduce cost of mailing by targeting a set of consumers

likely to buy a new cell-phone product.

– Approach:

• Use the data for a similar product introduced before.

• The {buy, don’t buy} decision forms the class attribute.

• Use this information as input attributes to learn a classifier model.

• Collect various demographic, lifestyle and company related information about all such customers.

Predictive Analysis: Application 1


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• Fraud Detection

- Goal: Predict fraudulent cases in credit card transactions.

- Approach:

• Use credit card transactions and the information of account-holder (When does a customer buy, what does he buy, how often he pays on time, etc )as attributes.

• Label past transactions as fraud or fair transactions. This forms the class attribute.

• Learn a model for the class of the transactions.

• Use this model to detect fraud by observing credit card transactions on an account.

Predictive Analysis : Application 2


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By incorporating data mining techniques in marketing DSS, retailers can improve their inventory logistics and thereby reduce their cost in handling inventory

DSS with visualization tools may help to understand the composition of the portfolio and help identify what changes need to be made in its component stocks.

Predictive Analysis : Other Applications


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Overview


Introduction






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Classification: DefinitionGiven a collection of records (training set )

–Each record contains a set of attributes, one of the attributes is the class.

Find a model for class attribute as a function of the values of other attributes.

Goal: previously unseen records should be assigned a class as accurately as possible.

–A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.


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General Approach for building classifier


Training Data

Learning Algorithm

Learn Model

Model(Classification Rules)

Test Data

Induction

Deduction Apply Model

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Association Rule Mining Association rule A B, where A, B I , and A B=.

The rule X Y has a support s in the transaction set D if s% of the transactions in D contain XY .

The rule X Y holds in the transaction set D with confidence c if c% of transactions in D that contain X also contain Y .

Strong rules are the rules that satisfy minimum support and confidence threshold values and this framework is known as the support confidence framework for association rule mining.


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Association Rule Discovery

(Data Mining , Statistics) Data Structures

support=20%, confidence=85%

Supermarket shelf management.

– Goal: To identify items that are bought together by sufficiently

many customers.

– A classic rule --

If a customer buys the book of ‘Data Mining’ and ‘Statistics , then he is very likely to buy the book of ‘Data Structure’ .

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Associative Classifiers (AC)

Associative Classifiers is a two step process :-

CAR generation(Association Rule Mining)

Classification using CAR


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Associative Classification (AC) Problem

Given a labeled training data set, the problem is to derive a set

of class association rules (CARs) from the training data set

which satisfy certain user-constraints, minimum support

(minsup) and minimum confidence (minconf).

Common Associative Algorithms:

CBA : Classification Based on Association Rule Ming

CPAR: Classification based on predictive association rule

CMAR: Classification based on Multiple Association Rules

MCAR: Multi-class classification based on association rule approach


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Temporal Associative Classifiers


The customer interest is changing with respect to time because of many reasons -

In real world, the items have the dynamic characteristic in terms of transaction, which have seasonal selling rate and it hold time-based associationship with another item.

For example during the festival time or during new year when the majority of customers are getting an improved earning or during the first ten days of a month (salaried employee) have the high purchasing tendency

Also during the depression / recession, the customer’s investment policy or purchasing tendency may change.

Also some items, which are infrequent in whole dataset may be frequent in a particular time period.

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Adopting temporal dimension will give more realistic approach and will yield much better and useful results in Predictive Analytics.

Hence, a novel approach of combining Associative Classifiers with temporal dimension is being proposed.The purpose of temporal predictive system is to provide the pattern or relationship among the items within time domain. For example rather than the basic association rule of {bread}{butter}, mining from the temporal data we can get a more insight rule that the support of {bread}{butter} raises to 50% during 7 pm to 10 pm everyday .


These type of rules are more informative and useful to make a strategic decision making in every field of business intelligence.

Temporal Associative Classifiers Cont……

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Overview


Introduction






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T.A.C.algorithm

Relational Data Base (Historical Data)

Prediction for business problem such as forecasting the customer Interest for given time granularity

Knowledge base for Predictive Model system in business

Intelligence solutions

Temporal Classification Association rules

Knowledge Management Module

Domain Knowledge(External

Resources)

Knowledge Management system using Temporal Associative Classifier

Methodology for Implementation


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Overview Introduction







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Experimental Result


Dataset CPAR TCPAR CMAR TCMAR CBA TCBA

Anneal 94.99 93.2 76.168 92.2 78.4 95.3

Breast 92.95 91.2 90.82 94.5 92.82 93.95

Heart 51.12 77.3 54.09 76.2 55.42 75.5

Hepatitis 74.34 72.1 78.33 74.4 42.5 77.5

Horse 81.57 88.3 67.47 89.4 56.16 79.8

Ionosphe 89.76 88.3 63.8 86.3 42.17 82.48

Iris 95.33 93.4 96 91.3 96 92.3

Pima 74.82 74.3 65.1 75.3 55.7 74.24

Wine 88.03 85.5 68.96 80.46 72.31 88.2

Zoo 95 93.2 40.36 92.3 60.18 88.2

Average Accuracy

83.791 85.68 70.1 85.236 65.16 84.747

Comparison of average accuracy for various associative Classifiers with their temporal counter part

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Overview Introduction







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Conclusion

1. This work presents a framework for Knowledge Management system and discusses the result of Temporal Associative Classifier.

2. TAC is found to be an effective technique to extract knowledge from the database .

3. The model presented in the paper highlights the amalgamation of TAC with Knowledge Management Approach.


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Future work

• The model can be implemented to perform prediction or classification in any sort of business problem in more efficient and accurate manner which will ultimately help in decision support system of any organisation.

2. In future the model can be further explored for the given application domain of super market sales to incorporate the domain experience of related experts.


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Thank you

ICMIS-2010 1 Knowledge Management Approach for Predictive ...

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