White
Paper: Data Mining - Beyond Algorithms
by Dr Akeel Al-Attar, MD of XpertRule Software
The case for data mining
Most organizations can be currently labelled 'data rich', since
they are collecting increasing volumes of data about business processes
and resources. Typically, these data mountains are used to provide
endless 'facts and figures' such are 'there are 60 categories of
occupation', '2000 mortgage accounts are in arrears' etc. Such 'facts
and figures' do not represent knowledge but if anything can lead
to 'information overload'. However, patterns in the data represent
knowledge and most organizations nowadays can be labelled 'knowledge
poor'. Our definition of data mining is the process of discovering
knowledge from data.
Data mining enables complex business processes to be understood
and re-engineered. This can be achieved through the discovery of
patterns in data relating to the past behaviour of a business process.
Such patterns can be used to improve the performance of a process
by exploiting favourable patterns and avoiding problematic patterns.
Examples of business processes where data mining can be useful
are customer response to mailing, lapsed insurance policies and
energy consumption. In each of these examples, data mining can reveal
what factors affect the outcome of the business event or process
and the patterns relating the outcome to these factors. Such patterns
increase our understanding of these processes and therefore our
ability to predict and affect the outcome.
Data Mining Technologies
There is a high degree of confusion among the potential users of
data mining as to what data mining technologies are. This confusion
has been compounded by vendors, of complimentary technologies, positioning
their tools as data mining tools. So we have many vendors of query
and reporting tools and OLAP (On-Line Analytical processing) tools
claiming that their products can be used for data mining. While
it is true that one can discover useful patterns in the data using
these tools there is a question mark as to who is doing the discovery
- the user or the tool! For example, query and reporting tools will
interrogate the data and report on any pattern (query) requested
by the user. This is a 'manual' and 'validation driven' method of
discovery in the sense that unless the user suspects a pattern they
will never find it! A marginally better situation is encountered
with the OLAP tools, which can be termed 'visualisation driven'
since they assist the user in the process of pattern discovery by
displaying multi-dimensional data graphically. The class of tools
that can genuinely be termed 'data mining tools' are those that
support the automatic discovery of patterns in data.
We are going to make one more assertion regarding the difference
between data mining and data modelling. Data mining
is about discovering understandable patterns (trees, rules
or associations) in data. Data modelling is about discovering a
model that fits the data, regardless of whether the model is understandable
- (e.g. tree or rules) or a black box (e.g. neural network). Based
on this assertion, we restrict the main data mining technologies
to induction and the discovery of associations and clusters.
Rule Induction
Rule or decision tree induction is the most established and effective
data mining technologies in use today. It is what can be termed 'goal
driven' data mining in that a business goal is defined and rule induction
is used to generate patterns that relate to that business goal. The
business goal can be the occurrence of an event such as 'response
to mail shots' or 'mortgage arrears' or the magnitude of an event
such as 'energy use' or 'efficiency'. Rule induction will generate
patterns relating the business goal to other data fields (attributes).
The resulting patterns are typically generated as a tree with splits
on data fields and terminal points (leafs) showing the propensity
or magnitude of the business event of interest.
 As
an example of tree induction data mining consider this data table
which represents captured data on the process of loan authorisation.
The table captures a number of data items relating to each loan
applicant (sex, age, time at address, residence status, occupation,
time in employment, time with the bank and monthly house expenses)
as well as the decision made by the underwriters (accept or reject).
The objective of applying rule induction data mining to this table
is to discover patterns relating the decisions made by the loan
underwriters to the details of the application.
 Such
patterns can reveal the decision making process of the underwriters
and their consistency, as shown in this tree. It reveals that the
time with the bank is the attribute (data field) considered most
important with a critical threshold of 3 years. For applicants that
have been with the bank over 3 years the time in employment is considered
the next most important factor, and so on. The tree below reveals
5 patterns (leafs) each with an outcome (accept or reject) and a
probability (0 - 1). High probability figures represent consistent
decision making.
The majority of data miners who use tree induction will most probably
use as in automatic algorithm which can generate a tree once the
outcome and the attributes are defined. Whilst this is a reasonable
first cut for generating patterns from data, the real power of tree
induction can be gained using the interactive (incremental) tree
induction mode. This mode allows the user to impart his/her knowledge
of the business process to the induction algorithm. In interactive
induction, the algorithm stops at every split in the tree (starting
at the root) and displays to the user the list of attributes available
for activating a split, with these attributes being ranked by the
criteria of the induction engine for selecting attributes (significance,
entropy or a combination of both). The user is also presented with
the best split of attribute values (threshold or groups of values)
according to the algorithm. The user is then free to select the
top ranking attribute (and value split) according to the algorithm
or select any other attribute in the ranked list. This allows the
user to override the automatic selection of attributes based on
the user's background knowledge of the process. For example, the
user may feel that the relationship between the outcome and the
best attribute is a spurious one, or that the best attribute is
one that the user has no control over and should be replaced by
one that can be controlled.
Interactive induction can also be seen as bridging the gap between
OLAP based manual data segmentation / exploration and algorithm
assisted segmentation.
The Discovery of associations
This is the second most common data mining technology and involves
the discovery of associations between the various data fields. One
popular application of this technology is the discovery of associations
between business events or transactions. For example discovering that
90% of customers that buy product A will also buy product B (basket
analysis) or that in 80% of cases when fault 1 is encountered then
fault 7 is also encountered. If the sequence of events is important
then another data mining technology for discovering sequences can
be used.
A second application of associations discovery data mining is
the discovery of associations between the fields of case data. Case
data is data that can be structured as a flat table of cases. Records
of mortgage applications is an example of case data. In such data,
associations can be found between data fields; for example that
75% of all applicants that are over 45 and in managerial occupations
are also earning over £40,000. Such associations can be used as
a way of discovering clusters in the data. Note that this differs
from rule induction on case data in that no outcome needs to be
defined for the discovery process.
Case Studies
A number of case studies are described in the following sections
which detail the background to each case studies, the data mining
approach used and the benefits gained by the organizations concerned.
Case Study 1: Mortgage Lending
This case study comes from a UK Mortgage Lender which had a mortgage
portfolio in which 9.8% of all accounts were in arrears (over 3 months
in arrears) and 4.1% of all accounts were in severe arrears (over
6 months in arrears). The objectives of the data mining project was
to discover patterns relating the propensity of arrears to the mortgage
application data. Such patterns can be applied at the front end applications
processing to reduce the level of arrears and can result in better
management of accounts that go into arrears.
Rule induction data mining was used with the outcome of the analysis
being the arrears status healthy, moderate arrears or severe arrears.
The attributes of the data mining analysis were the mortgage application
data such as age, income, occupation, term, loan amount, region
etc. Two separate data mining analysis were carried out; one to
discover the patterns of arrears and the second to discover the
patterns of severe arrears. Rule induction generated the following
tree for arrears with splits on the attributes %Adv (% of loan to
property value), SecondCh (second charge on property), Owned (is
the property already owned), sub/Inc (subscription to income), AppSource
(application source) and Region. The tree reveals 10 profiles with
a propensity for arrears ranging from 0.02 to 0.49.
The trees discovered from the arrears data was used in three ways:
- To generate policy rules which were introduced at the application
processing stage to reduce arrears.
- To formulate a marketing strategy targeting low risk profiles.
- To focus arrears management resources on accounts most likely
to end up in severe arrears.
Case Study 2: Life Underwriting
This case study is from the Hibernian in Ireland who like other Life
Insurers were facing the challenges of reducing costs, maintaining
market share and meeting market demands. In order to meet these challenges,
Hibernian decided to re-engineer its Life Underwriting Process in
order to speed up the process and reduce its costs.
The first phase of re-engineering involved the implementation
of a rule based underwriting system which was used to automate the
processing of Life Proposals at the point of application. The system
involved capturing underwriting knowledge which resulted in 51%
of proposals being underwritten automatically with the remaining
cases being referred to head office for manual underwriting.
While the automated underwriting system proved very successful,
Hibernian looked for ways of increasing the percentage of cases
that can be processed by the system. Attempts were made to capture
more advanced underwriting rules, however this proved to be very
difficult. Data mining was then considered as an alternative for
generating additional knowledge. The basic premise was that out
of the 49% of cases being referred to Head Office a significant
number were underwritten with no or a very small additional premium
(less than the cost of the manual underwriting!). It was therefore
decided to apply rule induction analysis to cases being referred
to Head Office with the amount of additional underwriting premium
being used as an outcome. Rule induction analysis generated patterns
of low additional premiums of the following format :
If AGE > 30 & AGE < 41 and HEIGHT-WEIGHT = NORMAL Then
PREMIUM LOADING = 1% The generated patterns for low additional
premiums were qualified and checked for risk by the actuaries at
Hibernian before being added as additional underwriting rules to
the automated underwriting system. The result was to increase the
rate of automated underwriting to 78%.
This case study illustrates how data mining helped Hibernian in
Ireland develop new ways of processing life proposals and these
methods now underpin a cost effective new business process.
Case Study 3 : Gas Processing Plant
This project was carried out for an oil company and was based in a
remote US oil field location. The process investigated was a very
large gas processing plant which produces two useful products from
the gas from the wells, natural gas liquids and miscible injectant.
NGL is mixed with crude oil and transported for refining, and MI is
used to improve the viscosity of oil in the fields to improve crude
oil recovery.
The aim of the study was to use data mining techniques to analyse
historical process data to find opportunities to increase the production
rates, and hence increase the revenue generated by the process.
Approximately 2000 data measurements for the process are captured
every minute.
Rule induction data mining was used to discover patterns in the
data. The business goal for data mining was the revenue from the
Gas Process Plant, while the attributes of the analysis fell into
two categories:
- Disturbances, such as wind speed and ambient temperature, which
have an impact on the way the process is operated and performs,
but which have to be accepted by the operators.
- Control set points, these can be altered by the process operators
or automatically by the control systems, and include temperature
and pressure set points, flow ratios, control valve positions,
etc.
An important part of the process is where the incoming feed gas
is pre cooled with heat exchangers in two parallel process streams.
The Oil company has always believed that there is an opportunity
to improve process performance by altering the split of flows, however,
it was not sure in which way to split the flow and what the impact
will be on the revenue. Therefore flow split was put forward as
an attribute for data mining.
This is the tree generated by rule induction. It reveals patterns
relating the revenue from the process to the disturbances and control
settings of the process. In particular the impact of the flow_split
is revealed with a critical ratio of 1.25 : 1.
The benefits derived from the generated patterns include the identification
of opportunities to improve process revenue considerably (by up
to 4%). Mostly these involve altering control set points, such as
altering the flow splits. Some of the discovered knowledge can be
implemented without any further work and for no extra cost (e.g.
Altering flow splits). In other cases, it is necessary to provide
the operators with timely advice about the best combination of settings
for a given circumstance. This can be achieved cost effectively
by delivering the rules generated as part of an expert system.
The company is in the process of carrying out another rule induction
analysis with product quality as an outcome. The discovered patterns
will allow the plant to be operated at the maximum revenue possible
with acceptable product quality.
Case Study 4 - Energy Usage in a Power Station
ICI Thornton Power station, produces steam for a range of processes
on the site, and generates electricity in a mix of primary pass out
and secondary condensing turbines. Total power output is approximately
50 MW (i.e. a small power station). Fuel and water costs amount to
about £5 million a year (depending on site steam demands).
The objective of the data mining project was to identify opportunities
to reduce power station operating costs. Costs include the cost
of fuel (gas and oil to fire the boilers) and water (to make up
for losses). Electricity and steam are sold and represent a revenue.
Rule induction data mining was used for the project with the outcome
(goal) of the analysis being the net cost of steam per unit of steam
supplied to the site (i.e. the cost of the product). The attributes
fall into two categories; disturbances such as ambient temperature
and the site steam demand over which the operators have no control,
and control settings such as pressure and bled steam rates.
Here is a section of the induced tree revealing the variation of
steam cost with attribute values. It identifies the main contributors
to efficient operations as manifold pressure (i.e. pressure at primary
turbine inlet), steam flow to the secondary turbines and the total
site steam flow.
The benefits derived from the generated patterns include the identification
of opportunities to improve process revenue considerably (by up
to 5%). Mostly these involve altering control set points which can
be implemented without any further work and for no extra cost. Implementing
some of the opportunities identified needed additional controls
and instrumentation. The pay back for the additional controls would
be a few months.
The Current Issues in Data Mining
With real case studies of organizations deploying data mining as a
catalyst for enhancing and re-engineering their business processes,
data mining is now entering mainstream IT as a mature and tested technology.
With this phase of evolution data mining has moved beyond the debate
on algorithms and into the debate on usability. There are three main
issues which should be considered by any organization considering
the introduction of data mining; methodology, ease of use and performance
/ scalability.
Methodology
For data mining to gain wide acceptance, it is important to have a
step by step methodology for a data mining project. This ensures that
the benefits reported by seasoned data miners are repeatable by other
people in various business sectors. This can help dispel the belief
that data mining is a kind of 'black art' which can only be practised
by specialist. Such a methodology is beginning to emerge and there
is certainly wide agreement on the main steps of such a methodology.
These are :
- Problem analysis This step
involves analysing a business problem to assess if it is suitable
for tackling using data mining. If it is, then an assessment has
to be made of the availability of data, the data mining technology
to used (induction, associations, etc.) and how the results of
data mining will be deployed as part of the overall solution
- Data Preparation This step
involves extracting the data and transforming it to the format
required for the data mining algorithm. This involves, aggregation,
table joins, deriving new fields, data cleansing, etc.
- Data Exploration This step
precedes the actual pattern discovery stage. It involves visualisation
driven exploration and its aim is to give the user a good feel
for the data and to reveal any errors in the data preparation
/ extraction.
- Pattern Generation This
step involves using rule induction (automatic or interactive)
and associations discovery algorithms to generate patterns. This
step also involves validating and interpreting the discovered
patterns
- Pattern Deployment This
step involves deploying the discovered patterns as designed in
the problem analysis stage. Patterns are typically used in decision
support systems, to produce reports / guidelines, or to filter
data for further processing.
- Pattern Monitoring A main
premise for the deployment of data mining results is that the
future resembles the past and hence historic patterns can be applied
to future situations. This strategy is safe only if the historic
patterns are regularly monitored against new data to detect shifts
in these patterns at the earliest possible time.
Ease of Use
Data mining tools are increasingly used by computer literate business
users. This requires these tools to be no more difficult to use than
a spreadsheet program. Furthermore the data mining tool needs to support
all the steps of a data mining methodology. Finally, because of the
nature of data mining, the tool has to support extensive data and
patterns reporting and visualisation.
Performance and Scalability
With the decreasing costs of data processing and storage comes the
data rich organization. It is now common place for small and medium
sized organizations to hold gigabytes of data relating to a business
process. It is therefore essential that data mining tools can deliver
acceptable performance on large volumes of data regardless of the
computing platform / architecture being used. There are a number of
computing architectures for data mining
Client based data mining
 In
this architecture the data to be mined is downloaded (extracted) and
stored on the client machine (Windows 95 or NT). All the data preparation
and mining is carried out on the client. Until recently this approach
was limited to mining tens of thousands of records (in acceptable
times of under an hour). Recent advances has made it possible for
millions of records to be mined on a client in tens of minutes and
XpertRule Software's Profiler is an example of a data mining tool which
such a capability.
Two tier client server data mining
 In
this architecture the data is extracted and stored on a server but
is mined from the client machine(s). There two distinct flavours of
this architecture:
- Standard platform server (NT or Unix)
In this scenario the server is capable of running a data mining
engine which is invoked by the data mining front-end running on
a client machine. This is the most common architecture for 2 tier
data mining and can handle the mining of millions of records on
a high performance Unix or NT hardware.
- High Performance Dedicated server
In this scenario the server is a dedicated machine which can not
run a data mining engine (for example Teradata). Most data mining
tools can only address this situation by extracting (copying)
data from the dedicated server to another standard platform server.
This approach is not liked by users since in involve much data
and hardware duplication. Recent advances has been made whereby
the data in dedicated servers is mined in situ by the data mining
client firing intelligent queries at the server (Profiler from
XpertRule Software is an example of a tool with such capability).
Three tier client server data mining
 This
architecture typically involves a dedicated high performance server
(such as Teradata from NCR), a standard platform (Unix but increasingly
NT) middle tier and a number of data mining clients. Again there two
distinct scenarios
- Thin middle tier This scenario
is only feasible if the middle tier can have running on it a data
mining engine which is invoked by the client, but which can mine
the data in situ on the server. Again this involves the data mining
engine on the middle-tier firing intelligent queries at the server.
- Fat middle tier This scenario
involves the middle tier loading the data where it is mined on
the middle-tier and the results are passed on to the data mining
client.
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