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Transcript of Memory Based Reasoning
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PREPARED BY:
ASHUTOSH KUMAR
(00316204411)
BHARTI SHARMA
(00716204411)
MEMORY BASED REASONING
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WHAT IS MBR?
MBR stands forMemory Based Reason ing.
In Memory Based Reasoning results are based
on analogous situations in the past.
Our ability to reason from experience dependson our ability to recognize appropriate
examples from the past.
These examples can be of: Movies, Food ,
Traff ic patterns and rou tes.
We identify similar example(s) and apply what
we know/learned to current situation.
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These similar examples in MBR are referred to as
Neighbors.
Identifying similar cases from experience.
Applying the information from these cases to the
problem at hand.
MBR finds neighbors similar to a new record anduses the neighbors for classification and
prediction.
By following all these basic information about
MBR, we came to know that basically it means
taking decisions for future depending on the past
experiences.
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MBR uses known instances of a model to predict
unknown instances.
This data mining technique maintains a dataset ofknown records.
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MEMORY BASED REASONING ALGORITHM:
This algorithm works on the principle in which firstwe identify similar instances in the past, then we
use the past instances and apply the information
about those instances to the present.
The algorithm knows the characteristics of the
records in this training dataset.
When a new record arrives for evaluation, the
algorithm finds similar to the new record then
uses the characteristics of the neighbor for
prediction and classification.
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When a new record arrives at the data mining tool,
first the tool calculates the distance between this
record and the records in the training dataset.
The results determine which data records in the
training dataset qualify to be considered as
neighbors to the incoming data record.
Next, the algorithm uses a combination function tocombine the results of the various distance functions
to obtain final answer.
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TWO BASIC OPERATIONS OF MBR ALGORITHM:
Distance function : assigns a distance between
any two records.
Combination function : combines the results fromthe neighbors to arrive at an answer
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APPLICATIONS OF MBR:
Fraud detection.
Customer response prediction.
Medical treatments.
Classifying responses MBR can process free-
text responses and assign codes.
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STRENGTHS OF MBR:
It produces results that are readilyunderstandable.
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 aminimal amount of effort.
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The three main issues in solving a problem with MBR:
Selecting the most suitable historical recordsto form the training or base dataset.
Establishing the best way to compose the
historical record. Determining the two essential functions,
namely, the distance function and the
combination function.
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Weaknesses of Memory-Based Reasoning:
It is computationally expensive when doingclassification and prediction.
It requires a large amount of storage for the
training set.Results can be dependent on the choice of
distance function, combination function, and
number of neighbors.
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THANK YOU