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Lecture-30
What Can Data Mining Do
Our previous lecture was a brief introduction about the data mining. What we covered in lecture
29 was just the tip of iceberg. The lecture may have definitely created in you an excitement to
explore more about DM like me. So this lecture is meant to give you more insight of DM, what it
can do for us, what are its specific applications. I will give some real life examples to show the
power of DM, what are the problems that can be solved by DM.
There are a number of data mining techniques and the selection of a particular technique is highly
application dependent, although other factors affect the selection process too. So let's look at
some of the DM application are as or techniques.
CLASSIFICATION
Classification consists of examining the properties of a newly presented observation and
assigning it to a predefined class.
Assigning customers to predefined customer segments (good vs. bad)
Assigning keywords to articles
Classifying credit applicants as low, medium, or high risk
Classifying instructor rating as excellent, very good, good, fair, or poor
Classification means that based on the properties of existing data, we have made or groups i.e. we
have made classification. The concept can be well understood by a very simple example of
student grouping. A student can be grouped either as good or bad depending on his previous
record. Similarly an employee can be grouped as excellent, good, fair etc based on his tra ck
record in the organization. So how students or employees were classified? Answer is using the
historical data. Yes history is the best predictor of the future. When an organization conducts test
and interviews from candidate employees, their performanc e is compared with those of the
existing employees. The knowledge can be used to predict how good you can perform if
employed. So we are doing classification, here absolute classification i.e. either good or bad or in
other words we are doing binary class ification. Either you are in this group or this. Each entity is
assigned one of the groups or classes. An example where classification can prove to be beneficial
is in customer segmentation. The businesses can classify their customers as either good or bad;
the knowledge thus can be utilized for executing targeted marketing plans. Another example is of
a news site, where there are number of visitors and also many content developers. Now where to
place a specific news item on the web site? What should be the hierarchical position of the news
item, what should be the news chapter, category? Either it should be in the sports or weather
section and so on. What is the problem in doing all this? The problem is that it's not a matter of
placing a single news item. The site as already mentioned contains a number of content
developers and also many categories. If sorting is performed humanly, then it is time consuming.
That is why classification techniques can scan and process the document to decide its category or
class. How and what sort of processing will be discussed in the next lecture. It is not possible and
there are flaws in assigning category to any news document just based on the keyword. Frequent
occurrence of the word keyword cricket in a document doesn't necessary means that the
document be placed in the sports category. The document may be actually political in nature.
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ESTIMATION
As opposed to discrete outcome of classification i.e. YES or NO, deals with continuous valued
outcomes
Example:
Building a model and assigning a value from 0 to 1 to each member of the set.
Then classifying the members into categories based on a threshold value.
As the threshold changes the class changes.
Next category of problems that can be solved with DM is using es timation. In classification we
did binary assignment i.e. data items are assigned to either of the two categories or classes, this or
that. The assignment value was integer in nature, and to be absolute was not essential. However
in case of estimation a model/mechanism is formed then data analysis is performed in that model
which was actually formed from data itself. The difference is that the model is formed from the
relationships in the data and then data categorized in that model. Unlike classification,
categorization here is not absolute but there is a real number that is between 0 and 1. This number
tells the probability of a record/tuple/item etc. to belong to a particular group or category or class.
So this is a more flexible approach than classification. Now the question arises how a real number
between 0 and 1 reveals the probability of belonging to a class? Why not an item falls in two
groups or more at the same time? The answer is that categorization is performed by setting the
threshold values. It is predefined that if the value crosses this then in this class else in another
class and so on. Note that if thresholds are reset which is possible (as nothing is constant except
change so changes can be made in the threshold) then the category or clas s boundaries change
resulting in the movement of records and tuples in other groups accordingly.
PREDICTION
Same as classification or estimation except records are classified according to some predicted
future behavior or estimated value.
Using class ification or estimation on a training example with known predicted values and
historical data a model is built.
Then explain the known values, and use the model to predict future.
Example:
Predicting how much customers will spend during next 6 months.
Prediction here is not like a palmists approach that if this line then this. Prediction means that
what's the probability of an item/event/customer to go in a specific class. This means that
prediction tells that in which class this specific item would lie in future or to which class this
specific event can be assigned in any time in future, say after six years. How prediction actually
works? First of all a model is built using exiting data. The existing data set is divided into two
subsets, one is called the training set and the other is called test set. The training set is used to
form model and the associated rules. Once model built and rules defined, the test set is used for
grouping. It must be noted the test set groupings are already known but they are put in the model
to test its accuracy. Accuracy, we will discuss in detail in following slides but is dependent on
many factors like the model, training data and test data selection and sizes and many more things.
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So, the accuracy gives the confidence level, that the rules are accurate to that much level.
Prediction can be well understood by considering a simple example. Suppose a business wants to
know about their customers their propensity to buy/spend/purchase. In other words, how much
the customer will spend in next 6 months? Similarly a mobile phone company can install a new
tower based on the knowledge spending habits of its customers in the surroundings. It is not the
case that companies install facilities or invest money because of their gut feelings. If you think
like this you are absolutely wrong. Why companies should bother about their customers? Because
if they know their customers, their interests, their like and dislikes, their buying patterns then it is
possible to run targeted marketing campaigns and thus increasing profit.
MARKET BASKET ANALYSIS
Determining which things go together, e.g. items in a shopping cart at a super market.
Used to identify cross-selling opportunities
Design attractive packages or groupings of products and services or increasing price of
some items etc.
Next problem can be solved or being solved by DM is the market basket analysis. Market basket
analysis is a concept, like in big stores you may have seen baskets for roaming around and putting
selected items in it. The concept here is to know basically that which things are sold together.
Why the knowledge is needed for decision making? This can be beneficial because if we know
that these are the things which are sold together, or if we know that some type of customers
mostly buy item X too when they buy item Y and so on, then we can run corresponding sale
promotional schemes. This can be useful for inventory management i.e. you may place things that
are bought together in close proximity, or you can place those things close in your store so that
it's easy to bring things together when needed. Another benefit is that u can bundle or package
item together so as to boost the sales of underselling items. Customer satisfaction is critical for
businesses, so another benefit of market basket analysis is the customer facilitation. Thus, by
placing together the associated items you facilitate the customer making easy access to the
desired items. Otherwise he may rum here and there for searching the item.
MARKET BASK T ANALYSIS
E
98% of people who purchased items A and B
also purchased item C
Figure-30.1: Market basket analysis
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Lets consider an example for better understanding of the market basket analysis. Figure 30.1
shows a basket having items A , B and Y. The right side portion of the Figure shows different
items arranged in two columns and arrows show the associations i.e. if item in the left column is
bought then the respective item in the right column is also bought. How we came to know this?
This knowledge was hidden deep in the data, so querying was not possible because a store may
contain thousands of items and effort to find item associations trivially is impossible, an NP
complete problem.
Discovering Association Rules
Given a set of records, each containing set of items
 Produce dependency rules that predict occurrences of an item based on others
Applications:
 Marketing, sales promotion and shelf management
 Inventory management
Rules:
{Milk} {Cola}
{Diaper, Milk} {Juice}
Table -30.1: Discovering association rules
Discovering Association Rules is another name given to market basket analysis. Here rules are
formed from the dependencies among data items which can be used to predict the occurrence of
an item based on others e.g. suppose hardware shop where whenever a customer buys color tins it
is more likely that he /she will buy painting brushes too. So based on the occurrence or event of
paint purchase, we can predict the occurrence of item paint brush. What is the benefit of knowing
all this? We have already discussed this. Now look at the Table 30.1, here two columns TIC
(transaction ID) and other is the list of items. This is not the view of a real database, as a single
column can not contain multiple entries like items column here. This is an example table just to
show rule formation process. Looking at the table we come to know that whenever milk is
purchased, cola is also purchased. Similarly, whenever diaper and milk are purchased juice is also
purchased. So, the two association rules are obtained from the sample data in Table 30.1. Now a
question arises which of the two rules strongly implies? This can not be answered depending on a
lot of factors. However, we can tell what has been discovered here? What is the unknown
unknown? The discovery is that the sale of juice with diapers and milk is non trivial. This can
never be guessed because no obvious association is found among the items.
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CLUSTERING
Task of segmenting a heterogeneous population into a number of more homogenous sub-
groups or clusters.
Unlike classification, it does NOT depend on predefined classes.
It is up to you to determine what meaning, if any, to attached to resulting clusters.
It could be the first step to the market segmentation effort.
What else data mining can do? We can do clustering with DM. Clustering is the technique of
reshuffling, relocating exiting segments in given data which is mostly heterogeneous so that the
new segments have more homogeneous data items. This can be very easily understood by a
simple example. Suppose some items have been segmented on the basis of color in the given data.
Suppose the items are fruits, then the green segment may contain all green fruits like apple,
grapes etc. thus a heterogeneous mixture of items. Clustering segregates such items and brings all
apples in one segment or cluster although it may contain apples of different colors red, green,
yellow etc. thus a more homogeneous cluster than the previous cluster.
Clustering is a difficult task, why? In case of classification we already know the number of
classes, either good or bad or yes or no or any number of classes. We also have the knowledge of
classes properties so its easy to segment data into known classes. However, in case of clustering
we don't know the number of clusters a priori. Once clusters are found in the data business
intelligence, domain knowledge is needed to analyze the found clusters. Clustering can be the
first step towards market segmentation i.e. we can use countermining to know the possible
clusters in the data. Once clusters found and analyzed classification can be applied thus gaining
more accuracy than any standalone technique. Thus clustering is at higher level than classification
not only because of its complexity but also because it leads to classification.
Examples of Clustering Applications
Marketing: Discovering distinct groups in customer databases, such as customers who
make lot of long-distance calls and don't have a job. Who are they? Students. Marketers
use this knowledge to develop targeted marketing programs.
Insurance: Identifying groups of crop insurance policy holders with a high average claim
rate. Farmers crash crops, when it is "profitable".
Land use: Identification of areas of similar land use in a GIS database.
Seismic studies: Identifying probable areas for oil/gas exploration based on seismic data.
We discussed that what clustering is and how it works. Now to know the real spirit of it, lets look
at some of the real world examples to show the blessings of clustering;
1. Knowing or discovering about your market segment: Suppose a telecom company whose
data when clustered revealed that there is a group or cluster of people or customers whose
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long distance calls are greater in number. Is this a discovery that such a group exi sts? Nope
not really. The real discovery is analyzing the cluster, the real fun part. Why these people are
in a cluster? Is important to know. Analysis of the cluster reveals that all the people in the
group are unemployed! How come it is possible that unemployed people are making
expensive far distance calls? The excitement lead to further analysis which ultimately
revealed that the people in the cluster were mostly students, students like you living away
from home in universities , colleges and hostels. They are making calls back home. So this is
a real example of clustering. Now the same question what is the benefit of knowing al this?
The answer is customer is like an asset for any organization. To know the customer is crucial
for any organization/compa ny so as to satisfy the customer which is a key of any company's
success in terms of profit. The company can rum targeted sale promotion and marketing
effort to target customers i.e. students.
2. Insurance: Now lets have look at how clustering plays a role in insurance sector. Insurance
companies are interested in knowing the people having higher insurance claim. You may
astonish that clustering has successfully been used in a developed country to detect farmer
insurance abuses. Some of the malicious farmers used to crash their crops intentionally to
gain insurance money which presumably was higher than the amount of profit and effort from
their crops. The farmer was happy but the loss was to be bear by the insurance company. The
company successfully used clustering techniques to identify such farmers, and thus saving a
lot of money.
Clustering thus has a wider scope in real life applications. Other areas where clustering is being
used are for city planning, GIS (Land use management), seismic data for mining (real mining)
and the list goes on.
Ambiguity in Clustering
How many clusters?
o Two clusters
o Four clusters
o Six clusters
Figure-30.2: Ambiguity in Clustering
As we mentioned the spirit of clustering lies in its analysis. A common ambiguity in clustering is
regarding the number of clusters, since the cluster are not known in advance. To understand the
problem, consider the example in Figure 30.2. The black dots represent individual data records or
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tuples and they are placed as a result of a clustering algorithm. Now can u tell how many clusters
are there?
Yes two clusters, but look at your screens again and tell how many clusters now?
Yes four clusters now, you are absolutely right. Now look again and tell how ma ny clusters? Yes
6 clusters as shown in the Figure 30.2. What all this shows? This shows that deciding upon the
number of clusters is a complex task depending on factors like level of detail, application domain
etc. By level of detail I mean that either the black point represents a single record or an aggregate.
The thing which is important is to know how many clusters solve our problem. Understanding
this solves the problem.
DESCRIPTION
Describe what is going on in a complicated database so as to increas e our understanding.
A good description of a behavior will suggest an explanation as well.
Another application of DM is description. To know what is happening in our databases is
beneficial. How? The OLAP cubes provide ample amount of information, which is otherwise
distributed in the haystack. We can move the cube in different angles to get to the information of
interest. However, we might miss the angle which might have given use some useful information.
Description is used to describe such things.
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Comparing Methods (1)
Predictive accuracy: this refers to the ability of the model to correctly predict the class
label of new or previously unseen data
Speed: this refers to the computation costs involved in generating and using the method.
Robustness: this is the ability of the method to make correct predictions/groupings given
noisy data or data with missing values
We discussed different data mining techniques. Now the question, which technique is good and
which bad? Or say like which is the best technique for a given problem. Thus we need to specify
evaluation criteria like data metrics as we did in the data quality lecture. The metrics we use for
comparison of DM techniques are;
Accuracy: Accuracy is the measure of correctness of your model e.g. in classification we have
two data sets, training and test sets. A classification model is built based on the data properties
and relationships in training data. Once built the model is tested for accuracy in terms of %
correct results as the classification of the test data is already known. So we specify the correctness
or confidence level of the technique in terms % accuracy.
Speed: In previous lectures we discussed the term "Need for Speed". Yes speed is a crucial
aspect of Dm techniques. Speed refers to the time complexity. If a technique has O (n) and
another has O (n log n) time complexities then which is better? Yes O (n) is better. This is the
computational time but user or business decision maker is interested in the absolute clock time.
He has nothing to do with complexities. What he is interested in is, knowing how fast he gets the
answers. So just comparing on the basis of complexities is not sufficient. We must look at the
overall process and interdependencies among tasks which ultimately result in the answer or
information
generation.
Robustness: It is the ability of the technique to work accurately even in conditions of noisy or
dirty data. Missing data is a reality and presence of noise also true. So a technique is better if it
can run smoothly even in stress conditions i.e. with noisy and missing data.
Scalability:  As we mentioned in our initial lectures that the main motivation for data
warehousing is to deal huge amounts of data. So scaling is very important, which is the ability of
the method to work efficiently even when the data size is huge.
Interpretability: It refers to the level of understanding and insight that is provided by the
method. As we discussed in clustering one of the complex and difficult tasks is the cluster
analysis. The techniques can be compared on the basis of their interpretational ability e.g. there
might be some methods which give additional functionalities to provide meaning to the
discovered information like color coding, plots and curve fittings etc.
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Where does Data Mining fits in?
Data Mining is one step of
Knowledge Discovery in
Databases (KDD)
Figure-30.3: Where does Data Mining fits in?
Now lets look at the overall knowledge discovery KD process. Figure 30.3 shows different KDD
steps. Data is crucial and the most important component of KDD, knowledge discovery is
possible only if data is available. The data is not used in its crude form for knowledge discovery.
Before applying analysis techniques like data mining, data is preprocessed using activities as
discussed in ETL. You ca n see an additional process at the preprocessing step i.e. feature
extraction. This is to extract those data items which are needed or which or of interest form the
huge data set. Suppose we have an O (n2) method for data processing. Scalability can be issue for
large data sets because of quadratic nature. The problem can be solved if we perform aggregation,
reducing number of records and keeping the data properties. So now the method can work with
less data size. Next comes the data mining phase, we have thoroughly discussed the techniques
for discovering patterns (clustering) and generating models (classification). The next step is the
analysis of discovered patterns using domain knowledge. This is a complex task and requires an
ample amount of business or domain knowledge. The interpreted knowledge finally comes out as
the information that was unknown before hidden in the data sea which has now become
information having some value to the user.
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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, Orrs 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