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Lecture # 29
A Brief Introduction to Data mining (DM)
In our one of previous lectures, we discussed "putting the pieces together". One of the things in
those pieces was data mining. We mine data when we need to discover something out of a lot. It
is a broad discipline, dedicated courses being offered solely. However, we will go through a brief
introduction of the field so that we get ample knowledge about data mining concepts, applications
and techniques.
What is Data Mining?: Informal
"There are things that we know that we know...
there are things that we know that we don't know...
there are things that we don't know we don't know."
Donald Rumsfield
US Secretary of Defence
Lets start data mining with a interesting statement. Why interesting because the
statement covers the overall concept of DM and is given by a non-technical person who
neither is a scientist nor a data mining guru. The statement, given by Donald Rumsfeld,
Defense Secretary of the USA in an interview, is as under.
As we know, there are known knowns. There are things we know that we know like you know
your names, your parent's names. We also know there are known unknowns. That is to say, we
know that there are some things we do not know like what one is thinking about you, what you
will eat after six days, what will be result of a lottery and so on. But there are also unknown
unknowns, the ones we don't know that we don't know. Are they beneficial if you know? Or it is
harmful no to know them?
There are also unknown knowns, things we'd like to know, but don't know, but know someone
who can doctor them and pass them off as known knowns. To associate Rumsfeld's above
quotation with data mining, we identify four core phrases as
1.
Known knowns
2.
Known unknowns
3.
Unknown unknowns
4.
Unknown knowns
The items 1 3, and 4 deal with "Knowns". Data mining has relevance to the third point in red. It is
an art of digging out what exactly we don't know that we must know in our business. The
methodology is to first convert "unknown unkowns" into "known unknowns" and then finally to
"known knowns".
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What is Data Mining?: Slightly Informal
Tell me something that I should know.
When you don't know what you should be knowing, how do you write SQL?
You cant!!
Now a slightly technical view of DM. Not that much technical but you may easily understand.
Tell me something that I should know i.e. you ask your DWH, data reposit ory that tell me
something that I don't know, or I should know. Since we don't know what we actually don't
know and what we must know to know, we can't write SQL's for getting answers like we do in
OLTP systems. Data mining is an exploratory approach, where browsing through data using data
mining techniques may reveal something that might be of interest to the user as information that
was unknown previously. Hence, in data mining we don't know the results.
What is Data Mining?: Formal
§
Knowledge Discovery in Databases (KDD).
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Data mining digs out valuable non-trivial information from large multidimensional
apparently unrelated data bases (sets).
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It's the integration of business knowledge, people, information, algorithms, statistics and
computing technology.
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Finding useful hidden patterns and relationships in data.
Before looking into the technical or formal view of DM, consider the quote that you might have
heard in your childhood i.e. finding a needle in the haystack . It is a tough job to find a need le in a
big box full of hay. Dm is finding in the hay stack (huge data) the needle (knowledge). You don't
have idea about where the needle can be found or even you don't know whether the needle is
there in the haystack or not.
Historically, the notion of finding useful patterns in data has been given a variety of names,
including data mining, knowledge extraction, information discovery, information harvesting, data
archaeology, and data pattern processing. The term data mining has mostly been used by
stati ticians, data analysts, and the management information systems (MIS) communities. It has
s
also gained popularity in the database field. KDD refers to the overall process of discovering
useful knowledge from data, and data mining refers to a particular step in this process. Data
mining is the application of specific algorithms for extracting patterns from data. The additional
steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of
appropriate prior knowledge, and proper interpretation of the results of mining, is essential to
ensure that useful knowledge is derived from the data. Blind application of data-mining methods
(rightly criticized as data dredging in the statistical literature) can be a dangerous activity, easily
leading to the discovery of meaningless and invalid patterns.
An important to note here is the use of term discovery rather than finding. This is so because you
find something that you know you have lost, or you know that something exist but not in your
approach so you search for that and find. In DM as we are saying again and again you don't know
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what you are looking for. In other words you don't know the results, so the term discovery rather
than finding is used.
In the given definition, the 5 key words are;
Nontrivial
By nontrivial, we mean that some search or inference is involved; that is, it is not a
straightforward computation of predefined quantities like computing the average value of a set of
numbers. For example, suppose the majority of the customers of a garments shop are men. If
some women too buy garments then it's common and trivial because women sometimes buy for
their children and spouse. Similarly if "Suwaiyan" sale increases during Eid days, the sugar, milk
and date sales also in crease. The information again is trivial. If the sale of some items boosts up,
even when no Eid was around, in some region of the country, this is non -trivial information
Value
The term value refers to the importance of discovered hidden patterns to the user in terms of its
usability, validity, benefit and understandability. Data mining is a way to intelligently probing
large databases to find exactly where the value resides.
Multidimensional
By multidimensional we mean a database designed as a multidimensional hypercube with one
axis per dimension. In a flat or relational database, each field in a record represents a dimension.
In a multidimensional database, a dimension is a set of similar entities; for example, a
multidimensional sales database might include the dimensions Product, Time, and City. Data
mining Multidimensional databases allows users to analyze data from many different dimensions
or angles, categorize it, and summarize the relationships identified.
Unrelated
Humans often lack the ability to comprehend and manage the immense amount of available and
unrelated data. Data mining can help us take very small pieces of data that are seemingly
unrelated i.e. no relationship exits, and determine whether they are correlated and can tell us
anything that we need to know".
Business Knowledge
The domain business processes must be known apriority before applying defaming techniques.
Since data mining is an exploratory approach we can not know what we should know, until and
unless we are not well aware of the activities involved in current business processes.
People
Human involvement in the data mining process is crucial in sense that value of patterns is well
known to the user. Since data mining focuses on "unknown unkowns" , people factor plays a key
role in directing data mining probe in a direction that ultimately ends in something that is
previously unknown, novel, and above all of value. Thus, Data mining has proven to be a
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powerful tool capable of providing highly targeted information to support decision-making and
forecasting for people like scientists, physicians, sociologists, the military and business etc.
Algorithms
Data mining consists of algorithms for extracting useful patterns from huge data. Their goal is to
make prediction or/and give description. Prediction involves using some variables to predict
unknown values (e.g. future values) of other variables while description focuses on finding
interpretable patterns describing the data. These algorithms can sift through the data in search of
frequently occurring patterns, can detect trends, produce generalizations about the data, etc. There
are different categories of data algorithms based on their application, approach etc. Commonly
used are classification algorithms, clustering algorithms, rule based algorithms, and artificial
neural networks etc.
Statistics
Data Mining uses statistical algorithms to discover patterns and regularities (or "knowledge") in
data. For example: classification and regression trees (CART, CHAID), rule induction (AQ,
CN2), nearest neighbors, clustering methods, association rules, feature extraction, data
visualization, etc.
Data mining is, in some ways, an extension of statistics, with a few artificial intelligence and
machine learning twists thrown in. Like statistics, data mining is not a business solution, it is just
a technology.
Computing Technology
Data mining is an inter disciplinary approach having knowledge from different fields such as
databases, statistics, high performance computing, machine learning, visualization and
mathematics to automatically extract concepts, and to determine interrelations and patterns of
interest from large databases.
Why Data Mining?
HUGE VOLUME THERE IS WAY TOO MUCH DATA & GROWING!
Data collected much faster than it can be processed or managed. NASA Earth Observation
System (EOS), will alone, collect 15 Peta bytes by 2007 (15,000,000,000,000,000 bytes).
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Much of which won't be used - ever!
§
Much of which won't be seen - ever!
§
Why not?
§
There's so much volume, usefulness of some of it will never be discovered
SOLUTION: Reduce the volume and/or raise the information content by structuring, querying,
filtering, summarizing, aggregating, mining...
Data Mining is the exploratory data analysis with litt le or no human interaction using
computationally feasible techniques, i.e., the attempt to find interesting structures/patterns
unknown a priori
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The ability of data mining techniques to deal with huge volumes of data is a distinguishing
characteristic. The data volumes of organizations/enterprises/businesses are increasing with a
greater pace e.g. NASA Earth Observing System (EOS) generates more than 100 gigabytes of
image data per hour, stored in eight centers. Data collected much faster than it can be processed
or managed. NASA EOS will alone, collect 15 Peta bytes by 2007 (15,000,000,000,000,000
bytes). Plunging in to such a deep and vast sea of data and performing analysis is not possible
through conventional techniques. So majority of the data will remain untouched, unseen, unused
thus limiting the discovery of useful patterns. This requires the availability of tools and
techniques that can accommodate the huge volumes of data and its complexity. Obviously, data
mining is the best fit. It works by reducing the data volume and/or raise the information content
user interest by structuring, querying, filtering, summarizing, aggregating, mining etc. By raising
information content means that the those patterns are built and brought to surface that are
otherwise hidden and scattered in the data sea and of user interest.
Claude Shannon's info. theory
More volume means less information
Claude Shannon's theory states that as the volume increases the information content decreases
and vice versa.
De ci sion (Y/N)
(Machine Decides)
De ci sion Support
(Machine Helps Choose Rules)
Knowledge
(Machine Discovers Rules)
Value
of
In forma tion
(Machine aggregateshi-lights/
/
Data
correlates/summarizes)
Indexed Da ta
(Machine finds directly w/o Scan)
Ra w Da ta
(Machine Scans)
Volume of
Data
Figure-29.1: Claude Shannon's info. theory
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The Figure 29.1 well illustrates the Shannon's Information Theory. At the base lies the raw data
having maximum volume. Here the data value, that increases as we go up (volume decreases), is
the minimum. Here exploring data for useful information needs conventional data scanning, thus
one is lost in the deep blue sea, reaching no-where.
In the next level is the indexed data. Data indexing has greatly reduced the data volume as
compared to the data in the lower level (raw data). Now we have found short cuts, to reach
desired points in the voluminous data sea, rather than conventional scanning. The data has more
value than the data at lower level.
In the next level is the aggregate/summarized data. Here the data is in a form that can readily be
used as information. Thus much compact volume and greater data value than the previous lower
levels.
Next is the level where the machine discovers and learns rules. The rules can easily be applied to
extract only desired data, or knowledge base thus greatly reducing the data volume as compared
to other lower levels.
The next is the level where machine supports decision making process by helping in selecting
appropriate pre defined rules. Here d ata volume is minimal and similarly higher data value than
the lower levels.
The final top most level is where the machine itself makes decisions based on predefined rules.
Those most appropriate rules are selected by machine itself for making a decision.Here of course
the data volume is minimum and the value of data is thus maximum.
Why Data Mining?: Supply & Demand
Amount of digital data recording and storage exploded during the past decade
BUT
number of scientists, engineers, and analysts available to analyze the data has not grown
correspondingly.
Another reason of Data mining is that the data generation rate far exceeds the data capturing and
analysis rate. In other words, the amount of digital data storage in different organizations world
wide has greatly increased in the past few decades. However, the number of scientists, engineers
and analysts for data analysis has not grown accordingly i.e. the supply of desired scientists and
researchers don't meet their high demand in high numbers, so that the huge and continuously
increasing data can be consumed or analyzed. Thus data mining tools provide a way, enabling
limited scientists and researchers to analyze huge amounts of data.
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Why Data Mining?: Bridging the gap
Requires solution of fundamentally new problems, grouped as follows:
1. developing algorithms and systems to mine large, massive and high dimensional data
sets;
2. developing algorithms and systems to mine new types of data (images, music, videos);
3. developing algorithms, protocols, and other infrastructure to mine distributed data; and
4. improving the ease of use of data mining systems;
5. developing appropriate privacy and security techniques for data mining.
Data mining evolved as a mechanism to cater the limitations of OLTP systems to deal massive
data sets with high dimensionality, new data types, multiple heterogeneous data resources etc.
The conventional systems couldn't keep pace with the ever changing and increasing data sets.
Data mining algorithms are built to deal high dimensionality data, new data types (images, video
etc.) , complex associations among data items , distributed data sources and associated issues
(security etc.)
Data Mining is HOT!
§  10 Hottest Jobs of year 2025
Time Magazine, 22 May, 2000
§
10 emergi ng areas of technology
MIT's Magazine of Technology Review, Jan/Feb, 2001
The TIME Magazine May 2000 issue has given a list of the ten hottest jobs of year 2025. Data
miners and knowledge engineers were at 5h a n d 6h position respectively. The proposed
t
t
course/Curriculum will cover Data Mining. Hence Data mining is a hot field having wide market
opportunities.
Similarly, MIT's Technology Review has identified 10 emerging areas of technology that will
soon have a profound impact on the economy and how we live and work. Among the list of
emerging technologies that will change the world, Data mining is at the 3r d place.
Thus in view of the above facts,  data miners have a long career in national as well a
s
international market as major companies both private and government are quickly adopting the
technology and many have already adopted.
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How Data Mining is different?
n Knowledge Discovery
--Overall process of discovering useful knowledge
n Data Mining (Knowledge-driven exploration)
-- Query formulation problem.
-- Visualize and understand of a large data set.
-- Data growth rate too high to be handled manually.
n Data Warehouses (Data-driven exploration):
-- Querying summaries of transactions, etc. Decision support
n Traditional Database (Transactions ):
-- Querying data in well-defined processes. Reliable storage
Conventional data processing systems or online transaction processing systems (OLTP) lie at the
bottom level. These systems have well defined queries and no any sort of knowledge discovery is
performed. OLTP systems are meant to support day to day transactions and do not support
decision making in any way. We can better understand with the analogy that when you travel
from your home to university you may follow a same route very often. While on the way you will
see same trees, same signals and same buildings every day provided you follow the same route. It
is not possible that each and every day you see different buildings, trees than the previous day.
Similar is the case for OLTP systems where you have well defined queries by running which you
know what sort of results you can get. Nothing new or no discoveries are here.
Data Mining provides a global macroscopic view or aerial view of your data. You can easily see
what you could not see at microscopic level. But before applying mining algorithms data must be
brought in a form so that the knowledge exploration from huge, heterogeneous and multi source
data can efficiently and effectively be performed. Thus DWH is the process of bringing input data
in a form that can readily be used by data mining techniques to find hidden patterns. Both terns
KDD and DM are sometimes used to refer to the same thing but K D refers to the overall
D
process from data extraction from legacy source systems, data preprocessing, DWH building,
data mining and finally the output generation. So KDD is a mega process having sub processes
like DWH and DM being its constituent parts.
How Data Mining is different...
Data Mining Vs. Statistics
§
Formal statistical inference is assumption driven i.e. a hypothesis is formed and validated
against the data.
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Data mining is discovery driven i.e. patterns and hypothesis are automatically ext racted
from data.
§
Said another way, data mining is knowledge driven, while statistics is human driven.
Although both of the two are for data analysis and none is good or bad, some of the difference
between statistics and Data mining are;
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Statistic s assumption driven. A hypothesis is formed using the historical data and is then
i
validated against current known data. If true the hypothesis becomes a model else the process is
repeated with different parameters. DM, on the other hand, is discovery driven i.e. based on the
data hypothesis is automatically extracted from the data. The purpose is to find patterns which are
implicit and hidden in the data sea otherwise. Thus data mining is knowledge driven while
statistics is human driven.
Data Mining Vs. Statistics
§
Both resemble in exploratory data analysis, but statistics focuses on data sets far smaller
than used by data mining researchers.
§
Statistics is useful for verifying relationships among few parameters when the
relationships are linear.
§
Data mning builds much complex, predictive, nonlinear models which are used for
i
predicting behavior impacted by many factors.
One difference is on the type of data. While statistician traditionally work with smaller and first
hand data" that has been collected or produced to check specific hypotheses, data miners work
with huge and second hand data" often assembled from different sources. The idea is to find
interesting facts and potentially useful knowledge hidden in the data and often unrelated to the
primary purpose why the data have been collected.
Statistics provides a language and framework for quantifying the uncertainty that results when
one tries to infer general patterns from a particular sample of an overall population. The concern
arose because if one searches long enough in any data set (even randomly generated data), one
can find patterns that appear to be statistically significant but, in fact, are not.
Statistics is useful only for data sets with limited parameters (dimensions) and simple
relationships (linear). Statistical methods fail when the data dimensionality is greater and the
relationships among different parameters are complex. Data mining proves to be viable solution
in such situations.
Thus, data mining is a legitimateflactontVshSttocuilids mx decseaseng capability to incarnate in them
ivi y t a b k ned oin lr havi
In ati
intricate data sets with greater dimensionality and complex associations that are non linear. The
models are used to predict behaviors impacted by different combinations of factors.Knowledge
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extraction using statistics
30
20
10
0
1.6
1.7
1.8
1.85
1.9
1.95
2
2.9
3
3.3
4.2
4.4
5
6
Inflation (%)
Q: What will be the stock increase when inflation is 6%?
A: Model non -linear relationship using a line y = mx + c. Hence answer is 13%
Figure-29.2: Knowledge extraction using statistics
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What statistics can offer for knowledge discovery? Consider the histogram in Figure 29.2. It
shows the relationship between %inflation and %stock increase. What if we want to know the
value of %stock increase at 6% inflation? W can use linear regression as shown by yellow line in
the figure. The line acts as a conducting wire, balanced between magnets (bars). Thus regression
here is like balancing of a wire, so that the final position of the line is as shown in Figure 29.2.
Thus we use curve fitting with linear regression and the line equatio n y=mx+c to calculate the
value at 6% inflation. Here m is the slope x is known 6% and c is the intercept.
Realistically only two variables here, however, in real life many variable may be more than 50. In
such situations linear regression, the statistical methods fail.
Failure of regression models
y = -0.0127x  6 + 1.5029x5 - 63.627x  4 + 1190.3x3 - 9725.3x  2 + 31897x - 29263
70000
60000
50000
40000
30000
20000
10000
0
0
5
10
15
20
25
30
35
-10000
Figure-29.3: Failure of regression models
To better understand the limitation of regression, consider a real life example in Figure 29.3.
Although l two variables here too, but the relationship is not that much simple. In real life the
association between variables is not that much simple rather complex as shown by the equation in
Figure -29.3. Here dotted line is the actual data. The non linearity of the association leads to the
dotted line structure having peaks and falls. Here we can't use linear regression like we did in our
previous example. Here we do a polynomial curve fitting like degree 6 curve fitting shown by
regular line in the figure. The red dotted boxes show the di fference between the actual and the
curve fitting. We can seen the difference is initially is less than what we can see in the next red
doted box in the middle. Thus the crux is that regression just failed even for two variables with
complex associations. How can it be applied to real life data having 20, 25 or even 50 attributes?
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Data Mining is...
§
Decision Trees
§
Neural Networks
§
Rule Induction
§
Clustering
§
Genetic Algorithms
Now lets discuss something about what is included in DM and what is not. First we will discuss
what DM is.
Decision Trees (DT): Decision trees consist of dividing a given data set into groups based on
some criteria or rule. The final structure looks like an inverted tree, hence the technique called
DT. Suppose a table having a number of records. The tree construction process will group most
related records or tuples in the same group. Decision at each node is taken based on some rule, if
this then this else this. Rules are not known a priori and are digged out of the training data set.
Clustering: It is one of the most important Dm techniques; we will discuss it in detail in coming
lectures. As a brief for understanding it involes the grouping of data items without taking any
human parametric input. We don't know the number of clusters and their properties a priori. Two
main types are one way clustering and two way clustering. One way clustering is when only data
records (rows) are used. Two way clustering is when all the rows and columns are being used for
clustering purpose.
Genetic Algorithms: These are based on the principle survival of the fittest. In these techniques,
a model is formed to solve problems having multiple options and many values. Briefly, these
techniques are used to select the optimal solution out of a number of possible solutions. However,
are not much robust as can not perform well in the presence of noise.
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Table of Contents:
  1. Need of Data Warehousing
  2. Why a DWH, Warehousing
  3. The Basic Concept of Data Warehousing
  4. Classical SDLC and DWH SDLC, CLDS, Online Transaction Processing
  5. Types of Data Warehouses: Financial, Telecommunication, Insurance, Human Resource
  6. Normalization: Anomalies, 1NF, 2NF, INSERT, UPDATE, DELETE
  7. De-Normalization: Balance between Normalization and De-Normalization
  8. DeNormalization Techniques: Splitting Tables, Horizontal splitting, Vertical Splitting, Pre-Joining Tables, Adding Redundant Columns, Derived Attributes
  9. Issues of De-Normalization: Storage, Performance, Maintenance, Ease-of-use
  10. Online Analytical Processing OLAP: DWH and OLAP, OLTP
  11. OLAP Implementations: MOLAP, ROLAP, HOLAP, DOLAP
  12. ROLAP: Relational Database, ROLAP cube, Issues
  13. Dimensional Modeling DM: ER modeling, The Paradox, ER vs. DM,
  14. Process of Dimensional Modeling: Four Step: Choose Business Process, Grain, Facts, Dimensions
  15. Issues of Dimensional Modeling: Additive vs Non-Additive facts, Classification of Aggregation Functions
  16. Extract Transform Load ETL: ETL Cycle, Processing, Data Extraction, Data Transformation
  17. Issues of ETL: Diversity in source systems and platforms
  18. Issues of ETL: legacy data, Web scrapping, data quality, ETL vs ELT
  19. ETL Detail: Data Cleansing: data scrubbing, Dirty Data, Lexical Errors, Irregularities, Integrity Constraint Violation, Duplication
  20. Data Duplication Elimination and BSN Method: Record linkage, Merge, purge, Entity reconciliation, List washing and data cleansing
  21. Introduction to Data Quality Management: Intrinsic, Realistic, Orr’s Laws of Data Quality, TQM
  22. DQM: Quantifying Data Quality: Free-of-error, Completeness, Consistency, Ratios
  23. Total DQM: TDQM in a DWH, Data Quality Management Process
  24. Need for Speed: Parallelism: Scalability, Terminology, Parallelization OLTP Vs DSS
  25. Need for Speed: Hardware Techniques: Data Parallelism Concept
  26. Conventional Indexing Techniques: Concept, Goals, Dense Index, Sparse Index
  27. Special Indexing Techniques: Inverted, Bit map, Cluster, Join indexes
  28. Join Techniques: Nested loop, Sort Merge, Hash based join
  29. Data mining (DM): Knowledge Discovery in Databases KDD
  30. Data Mining: CLASSIFICATION, ESTIMATION, PREDICTION, CLUSTERING,
  31. Data Structures, types of Data Mining, Min-Max Distance, One-way, K-Means Clustering
  32. DWH Lifecycle: Data-Driven, Goal-Driven, User-Driven Methodologies
  33. DWH Implementation: Goal Driven Approach
  34. DWH Implementation: Goal Driven Approach
  35. DWH Life Cycle: Pitfalls, Mistakes, Tips
  36. Course Project
  37. Contents of Project Reports
  38. Case Study: Agri-Data Warehouse
  39. Web Warehousing: Drawbacks of traditional web sear ches, web search, Web traffic record: Log files
  40. Web Warehousing: Issues, Time-contiguous Log Entries, Transient Cookies, SSL, session ID Ping-pong, Persistent Cookies
  41. Data Transfer Service (DTS)
  42. Lab Data Set: Multi -Campus University
  43. Extracting Data Using Wizard
  44. Data Profiling