Ontario – Toronto GTA – University of Toronto
Corporation
Ontario
Toronto GTA
University of Toronto
Corporation
active
NO single analytical software package in today€™s marketplace
will provide a complete solution to your data mining and system
modeling needs. Most companies requiring sophisticated data
analysis use several technologies to achieve their desired
results.
Unfortunately most business data is approximate or uncertain yet
the mathematical techniques commonly used assume that numbers
are exact. To deal with this incorrect assumption, we use
statistical techniques based upon distributions of many of these
exact numbers. Most statistical techniques work properly with
only a limited range of different distributions. By giving up
the dependency upon exactness, IIC fuzzy techniques more closely
model the real situation and can produce significantly better
results in the magnitude of 10% or greater.
SEE BELOW SOME OF IIC€™s SUCCESS STORIES
Leading Canadian Financial Institution works with IIC€™s advanced
fuzzy engine
Financial services companies such as banks and credit unions
routinely investigate the creditworthiness of their clients in
order to determine what services may be offered to them. A case
in point is determining which type of credit card is appropriate
to the client€™s financial situation. Banks are interested in
determining the risk adjusted return to the bank given the
credit card product offered to the client.
In a series of prototype system modeling experiments IIC
determined the "risk adjusted return" for a set of 900+
clients.. Inputs included such variables as: checking balance,
saving balance, income bracket, mortgage, etc. With such input
and output variables, IIC built a fuzzy rule base with both
unsupervised and supervised learning to predict potential risk
adjusted return of a client based on his/her particular variable
values.
We identified low, medium and high levels of "risk adjusted
returns" for the bank with a high level of accuracy as compared
to the "logistic-curve" analysis that they are currently
applying.
Other areas within the organization have been identified that
can benefit from the IIC fuzzy engine include
Relationship Marketing (householding)
Customer Profiles
Direct Marketing
Marketing, Campaign Management
Client Retention
Risk Management including Credit Monitoring: Fraud and delinquency triggers
Predictive Analysis to increase profits from credit card holders
IIC works with World Renown Internet Technology Leader Current
practice among Internet Service Providers is to support their
customers with Best Effort level of service. Many ISPs have
Service Level Agreements that determine the Quality of Service
based on the Class of Service that was purchased. Failure to
meet the terms of the SLA may impose financial penalties on the
ISP based on any change in the level of service that is provided
to the customer. Agreements tend to be divided into three
classes of service: Gold, Silver, and Bronze. Examples would be
Gold for video on demand, Silver for voice and Bronze for text
data transmission.
ISPs, Network Equipment, and Network Software Suppliers are
seeking ways to increase their revenue and provide improved
customer service by better managing network resources through
the use of a network traffic flow decision support system. Such
systems must be able to predict network traffic demand and
allocate sufficient bandwidth for both backbone and network
nodes. Dynamic adjustment of bandwidth allocation must occur
within microseconds in order to alleviate network congestion.
Current practices see bandwidth allocation being manually
adjusted by network personnel during low utilization periods
IIC has demonstrated the ability to create fuzzy rule base
models that can accurately predict traffic demand and allocate
bandwidth dependant on the Class of Service to be provided to
the customer.
The bandwidth allocation can be done within a Virtual Path if
the IP provider does not want to invest in extra capital
equipment. ISPs willing to investigate technologies such as
wavelength switching at the nodes could be provided with
bandwidth allocation schemas between virtual paths. Local
Healthcare Researchers to more accurately determine drug dosages
In pharmacology, analysts are interested in determining dosage
level of medication to individuals based on a person's
particular attributes such as age, weight, etc. For example
recently, its become clear that all adults should not be given
the same dosage. Their age, weight and other attributes should
be taken into account in order to minimizing side effects and
induce better treatment. Currently, IIC is conducting comparison
experiments for lithium dose and serum concentration prediction
between fuzzy system modeling approaches and multiple linear
regression approaches. The input variables are level, sex, age,
weight, status and tetra cyclic antidepressants which determine
the dose of daily lithium carbonate in mg's where level is serum
lithium level in mmol/L.
IIC improves predictive capability for Continuous casting
Customer orders come in for various grades of steel, which
specify width, length, weight, due date, etc. If the molten
metal pored into the ladle of a continuous caster is changed
frequently in order to deliver the customer orders without
delay, a large amount of unspecified grade of steel is produced
between different customer orders which require distinct grades
of steel.
IIC has developed a model that minimizes the tardiness of
customer order delivery due dates and the amount of mix grade
(unwanted) steel produced between orders. When compared with a
multi-variable regression model, our fuzzy system model gave a
better prediction with a reduction of error at 11% level, i.e.,
multi-variable regression model gave %15
The IIC fuzzy engine is based upon more than 20 years of
research in the area of fuzzy logic application by Dr. Burhan
Tursken, a world leader in this field and current chief
scientific officer at IIC.
Country of Ownership: Canada
Year Established: 2000
Exporting: Yes
Primary Industry (NAICS): 541510 - Computer Systems Design and Related Services
Primary Business Activity: Manufacturer / Processor / Producer
Total Sales ($CDN): $500,000 to $999,999
Export Sales ($CDN): $1 to $99,999
Number of Employees: 11
Products:
Fuzzy logic data engine
Technology:
Data Mining and System Modeling Processes
The Ten Steps of Data Mining and System Modeling
Presented is the IIC process for extracting hidden knowledge
from a data warehouse, a customer information file, or a company
database.
1. Identify The Objective
Before we begin, you have to be clear on what you hope to
accomplish with our analysis.
We need to know in advance the business goal of the data mining
and system modeling
for your company. We need to establish whether or not the goals
are measurable. Some
possible goals are to: · find sales relationships between specific products or
services
· identify specific purchasing patterns over time
· identify potential types of customers
· find product sales trends.
@ These items are some of customer related processes for data
mining and systems
modeling project.
2. Select The Data
In working with you to help define your goals, the next step is
to select the appropriate data to meet this goal. This may be a
subset of your data warehouse or a data mart that contains
specific product information. It may be your customer
information file. We need to segment as much as possible the
scope of the data to be mined.
Here are some key issues.
· Is the data adequate to describe the phenomena that the
data mining analysis is
attempting to model?
· Can you enhance internal customer records with external
lifestyle and demographic data?
· Is the data stable€”will the mined attributes be the same
after the analysis?
· If you are merging databases can you find a common field
for linking them?
· How current and relevant is the data to the business
goal?
@ Most of these (and similar) issues will probably be centered
around customer related processes. IIC works with the data
mining project manager and the IT personnel to decide on the
scope of the data to be mined.
3. Prepare The Data
Once the data has been assembled, a
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