Data Mining, Data Pattern, Machine Learning(Week 2

8/8/2019 Data Mining, Data Pattern, Machine Learning(Week 2

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DATA MINING

Data Mining, Data Pattern

and Machine Learning



Definition

• “…the analysis of (often large) observational data sets to find

unsuspected relationships and to summarize the data in novelways that are both understandable and useful to the dataowner.”

Hand, Mannila & Smyth

•

“… an interdisciplinary field bringing together techniques frommachine learning, pattern recognition, statistics, databases,and visualization to address the issue of information extractionfrom large data bases.”

Evangelos Simoudis in Cabena et al.

• “… the extraction of implicit, previously unknown, andpotentially useful information from data.”

Witten & Frank

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Why Has Data Mining Appeared

• Large volumes of data stored by organizations in a

competitive environment combined with advances intechnologies which can be applied to the data

• Background and evolution

–

• The need for exploratory data analysis

– Niche marketing, customer retention, the internet, onlineinteraction, scientific discovery

• The means to implement Data Mining – data warehouses, computing power, effective modelling

approaches

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Structural Pattern of Data

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Structural Pattern of Data --cont--

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Machine Learning

• To learn:

– To get knowledge of by study, experience, or beingtaught

– To become aware by information or from observation

–

o comm t to memory – To be informed

– To receive instruction

•

Learning: – Things learn when they change their behavior in a way

that makes them perform better in the future

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Machine Learning --cont--

• Machine Learning involves learning in

practical not in theoretical

• Interested in techniques for finding and

for helping to explain that data and make

predictions from it

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Data Mining

• Preliminary Analysis

– Much interesting information can be found byquerying the data set

– May be supported by a visualisation of the data set

•

Choose a one or more modelling approaches• There are (at least?) two styles of data mining

– Hypothesis testing

– Knowledge discovery

• The styles and approaches are not mutuallyexclusive

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The Proses of Knowlegde Discovery

• Pre-processing

– data selection

– cleaning

– codin

• Data Mining

– select a model

– apply the model

• Analysis of results and assimilation

– Take action and measure the results

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Data Selection

• Identify the relevant data, both internal and

external to the organisation

• Select the subset of the data appropriate for

• Store the data in a database separate from

the operational systems

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Data Pre-Processing

• Cleaning

– Domain consistency: replace certain values with

null

– -

database (DB) on each purchase transaction

– Disambiguation: highlighting ambiguities for a

decision by the user

• e.g., if names differed slightly but addresses were the

same

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Data Pre-Processing –cont--

• Enrichment

– Additional fields are added to records from externalsources which may be vital in establishingrelationships.

– e.g., take addresses and replace them with regionalcodes

– e.g., transform birth dates into age ranges

• It is often necessary to convert continuous datainto range data for categorisation purposes.

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Data Mining Task• Various taxonomies exist. E.g. Berry & Linoff 6 tasks:

– Classification

– Estimation (a.k.a. regression)

– Prediction

– Association Rule Discovery (a.k.a. Affinity Grouping )

– Clustering

– Description

• The tasks are also referred to as operations. Cabena et al. define 4 operations:

– Predictive Modelling

– Database Segmentation (a.k.a. clustering)

– Link Analysis

– Deviation Detection

• Beware! Different authors use different names for the same technique, operation

or task.

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Clasification

• Classification involves considering the

features of some object then assigning it it tosome pre-defined class, for example:

–

– Which phone numbers are fax numbers

– Which customers are high-value

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Regression

• Regression deals with numerically valued

outcomes rather than discrete categories asoccurs in classification.

–

– Estimating family income

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Prediction

• Essentially the same as classification and

estimation but involves future behavior

• Historical data is used to build a model

• The model developed is then applied to current

inputs to predict future outputs

– Predict which customers will respond to an

advertising promotion

– Classifying loan applications

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Association Rule Discovery

• Association Rule Discovery is also referred to

as Market Basket Analysis, or Affinitygrouping

• A common exam le is discoverin which

items are bought together at thesupermarket. Once this is known, decisionscan be made on, for example:

– how to arrange items on the shelves – which items should be promoted together

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Clustering

•

Clustering is also sometimes referred to assegmentation (though this has other meanings inother fields)

• In clustering there are no pre-defined classes. A

similarity measure is used to group records. The usermust attach meaning to the clusters formed

• Clustering often precedes some other data miningtask, for example:

– once customers are separated into clusters, a promotionmight be carried out based on market basket analysis of the resulting cluster

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Deviation Detection• Records whose attributes deviate from the norm

by significant amounts are also called outliers• Application areas include:

– fraud detection

–

– tracing defects

• Visualization techniques and statisticaltechniques are useful in finding outliers

• A cluster which contains only a few records mayin fact represent outliers

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Documents

Data Mining, Data Pattern, Machine Learning(Week 2