memory based reasoning_bia

7/30/2019 Memory Based Reasoning_BIA

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BY : MOHIT YADAV (096 )JAYEETA CHATTERJEE ( 101)MONIKA KATARIA ( 112 )


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MEANING OF DATA MINING

Data mining (the analysis step of

the knowledge discovery in databases process),

a relatively young and interdisciplinary field

of computer science is the process of

discovering new patterns from large data

sets involving methods at the intersection

of artificial intelligence, machinelearning, statistics and database systems.


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ROLE OF DATA MINING

Extract, transform, and load transaction data onto the data

warehouse system.

Store and manage the data in a multidimensional database

system.

Provide data access to business analysts and information

technology professionals.

Analyze the data by application software.

Present the data in a useful format, such as a graph or table.


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EXAMPLE OF DATA MINING


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ADVANTAGES AND

DISADVANTAGES OF DATAMINING

Marketing / Retail

Finance / Banking

Manufacturing Government

Privacy Issues Security Issues

Misuse of Information / Inaccurate Information


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Memory-Based Reasoning (MBR) tries to mimic human behavior in an

automatic way. Memories of specific events are used directly to make

decisions, rather than indirectly (as in systems which use experience to infer

rules). MBR is a two step procedure: first, identifying similar cases from

experience, secondly, applying the information from these cases to new

cases. MBR is specifically well suited to non-numerical data. MBR needs

a distance measure to assign dissimilarity of two observations and

a combination function to combine the results from the neighboring points

to achieve an answer. Generating examples is much easier than generatingrules which makes MBR so attractive. However, applying rules to new

observations is much easier and faster than comparing new cases to a bulk

of memorized objects.

MEMORY BASED REASONING

TECHNIQUE - MEANING


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The human ability to reason from experience depends on

the ability to recognize appropriate examples from the past.

A doctor diagnosing diseases, a claims analyst identifying

fraudulent insurance claims, Each first identifies similar

cases from experience and then applies knowledge of thoseexamples to the problem at hand. This is the essence of

memory-based reasoning. A database of known records is

searched to find preclassified records similar to a new

record. These neighbors are used for classification andestimation.


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ELEMENTS OF MBR

It uses known instances of a model to predictunknown instances.

Maintains a dataset of known records.

When a new record arrives for evaluation, the

algorithm finds neighbors similar to new record

which helps in :

Prediction Classification


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HOW IT WORKS?

When a new record arrives, the tool first

calculates the distance between new record

and the records existing in the training dataset. The distance function does the calculation.

This determine which training dataset qualify

to be considered as neighbors.


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SOLVING A DATA MINING

PROBLEM USING MBR

Selecting the most suitable historical records to

form the training or base dataset.

Establishing the best way to compose the

historical record.

Determining the two essential functions:

Distance

Function

Combination

Function


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MBRAPPLICATIONS

Fraud detection

Customer response prediction

Medical treatments

Classifying responses MBR can process

free-text responses and assign codes

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PREDICTIVE DATA MINING

USED IN MBR

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Tridas Vickie MikeHonest

BarneyWaldoWallyCrooked


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PREDICTION

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Tridas Vickie Mike

Honest = has round eyes anda

smile


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ADVANTAGES

Can use data asis.

Able to adapt easily to new data.

Adding/deleting example does not give sideeffect.

Explanation of answers is based on realexamples.

It is possible to apply to ordered data as well as Nominal data and ratio data.

High parallelism is possible.


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DISADVANTAGES

Resource intensive

No ability to generate the answer that does notexist in the examples data base.

Prediction accuracy strongly depends on thedefinition of similarity.

Choosing appropriate historical data for use intraining

Choosing the most efficient way to represent thetraining data

Choosing the distance function, combinationfunction, and the number of neighbors


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CONCLUSION

It produces results that are readily understandable.

It is applicable to arbitrary data types, even non-relational data.

It works efficiently on almost any number of fields.

Maintaining the training set requires a minimalamount of effort.

It is computationally expensive when doingclassification and prediction.

It requires a large amount of storage for the trainingset.

Results can be dependent on the choice of distancefunction, combination function, and number of

neighbors.


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memory based reasoning_bia

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