Module : NCG189: Intelligent Systems
Name : Tan Yee Mei (Jazz)
Student ID : 10034482
KDU Student ID : C15425
Assignment : Activity 1
Describe an example of an artificial intelligent system. Identify as precisely as possible
that aspect of the behaviour that makes it ‘intelligent’. Explain how that intelligence is
achieved. Include references to the original research where possible. Write a short
essay on the topic approximately 1500 words. [20].
STATISTICA is a software created by Statsoft. It is a software that does data mining and
analysis on its data (StatSoft Inc, 2012). In this case study, the STATISCA software that
will be focusing on would be STATISTICA Solutions for marketing purposes. Companies
uses the system for their marketing departments to use the STATISCA Data Miner to
help in the customer’s profiles, conduct effective marketing strategies, create more
opportunities to do cross-selling and up-selling, forecast results with consumer
behaviour and dealing with optimal inventory and packaging (STATISTICA Solution for
Marketing, 2011).
As artificial intelligence is being described by John McCarthy(2007), he stated
that, “Artificial Intelligence is the science and engineering of making the machines
intelligent, especially in computer programs”. Therefore this can be defined with
STATISTICA as a program that can help the marketing department in their business to do
predictive consumer behaviours and strengthening decision making on their marketing
strategies. This has been difficult to implement as the companies have to provide
previous results in order to input the program with data before it could actually have
the data to be analyst. Other than that, the system is capable of predicting future
customers purchases based on the given data with its programs running in the backend.
Based on STATISTICA themselves, they said that the STATISTICA Data Miner’s Interactive
Drill-Down Explorer allows it to review many types of purchases that a customer would
normally make. It is done with different characteristics, studying the group, ages and
extract info such as the likeness for the consumer for a new product from the previous
customers with the study of the current market by the drill-down analysis (STATISTICA
Solution for Marketing, 2011).
The system is also known to be intelligent because it is programmed in a way
that it runs on data mining technique (Statsoft, 2011). The STATISTICA Data Miner’s
Regression Modelling and Classification tools which includes an intelligence of
constructing decision trees such as the C&RT, neural networks, boosting and bagging for
the predictive data mining. It is also intelligent in a way of clustering and mapping down
multi-dimensional scaling and correspondence analysis. It also plays around the value
which enables it to process rapidly huge data sets of the customer’s transactions.
Referring to Data Mining Techniques (2011), it is defined as an analytic process in
STATISTICA to explore huge amount of data and especially in business in order to search
for patterns or systematic relationships between the data. The part where the system is
identified as intelligent would be the predictive data mining which consists of 3 stages.
The stages are initial exploration, model building and identification of patterns and
deployment.
Stage 1(Exploration):
According to the Data Mining Techniques (2011), this stage usually starts with the
preparation of the data which needs to be selected such as the subsets of records. It is
then send to perform some preliminary feature selection operations to bring the large
variables into a manageable range. Depending on the analytical problem, this may
involve straightforward predictors for a regression mode using a wide variety of
graphical and statistical methods in order to identify the complexity of the nature
models that can be taken into the next stage.
Stage 2(Model Building and identification of patterns):
This is the stage that uses different models and choosing the best model which based on
their predictive performances such as producing results among samples. It may look like
a simple operation but it is in fact that it sometimes involves a lot of elaboration
process. There are variety of techniques to achieve the goals which applies different
models to the same data sets and comparing it to choose the best performances. These
techniques include the predictive data mining such as bagging, boosting, stacking and
Meta-Learning.
Bagging is a concept of voting for classification, averaging the problems with
continuous dependent variables of interest applies to the area of predictive data mining
and to combine multiple models or from the same type for different learning data. In
practice, different trees can be grown to a full fledge data. A sophisticated algorithm in
the machine learning will generate weights for the prediction or voting in boosting
procedures.
Boosting is a concept that applies to the predictive data mining in order to
generate the multiple models or classifiers such as the C&T or CHAID to the learning
data. It assign greater weight to the observations that is difficult to classified compare to
the easy ones and continue its iteration.
Stacking on the other hand, is a concept that applies to the predictive data
mining by combining predictions from different models. It is really useful provided it
included the projects in many different ways.
Meta-Learning applies to the area of predictive data mining which combines the
predictions from multiple models. It is also refered to Stacking as mention
previously(Stack Generalization).
Stage 3(Deployment):
This is the final stage which involves the best model as stated in the previous stage to
new data and allowing it to generate predictions and estimates the expected outcomes.
This eventually leads to the Neural Network used in STATISTICA as well to perform
analytics on the data. Neural Networks is the analytic technique used to process the
hypothesised and modelled to handle the learning processes in the system and the neurological
functions of the brains which is used to observe. In another words, it learns from existing data.
Referring back to John McCarthy(2007), artificial intelligence is achieved through learning
experience and how the machine applies its knowledge onto a program. This explains why
STATISTICA is intelligent because it ability to learn from the models.
The first step in order to design the specific network architecture will include the layers
and neurons. The structure and size of the network needs to match the nature for example the
formal complexity of the phenomenon. This task is not easy as it involves trails and errors in the
system. However, the neural network applies the artificial intelligence to aid the task to find the
best network architectures. After that, it is subjected to training and in that phase itself, the
neurons apply its iterative process to the inputs or variables to adjust its weight of the network.
This is because it would want to optimally predict the sample with its training that is performed
on the data. After the phase of it being trained, it is now fit to perform predictive analyst to
output predicted data.
The result of the network developed in the process of learning will represents a pattern
in the data. Therefore, this method of approach is the functional equivalent to the model of
relations between variables of the traditional building model approach. Some neural networks
can sometimes produce really high accurate predictions that they represent. However in other
words of the theory would be the ‘black box’ research approach. Neural Network techniques
can also be used as a component designed to build explanatory models because it explore data
sets in search the variables and groups of variables. It will eventually facilitates the process of
model building process.
The system is also intelligent because of its advantages which they are capable of
approximating any continuous function and thus the researcher does not have any hypotheses
about the underlying model. Haykin(1994) defines neural network as a “massively parallel
distributed processor which has natural propensity for storing knowledge and making it
available for use. It resembles the brain in 2 ways. (1) Knowledge can be acquired through a
learning process and (2) connection of its weights are used to store the knowledge”.
On Ripley(2002) note on the other hand, he points out that the majority of the nueral
network application is run on a single-processor computer, therefore he argues that the large
speed can be achieved not only by developing its software and that it will take advantage of the
hardware also by designing better the algorithms for the machine to learn.
Reference
1. Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.
2. Data Mining Techniques (2011) Available at: http://www.statsoft.com/textbook/datamining-techniques/ (Accessed on : 12thFebruary 2012)
3. Edelstein, H., A. (1999). Introduction to data mining and knowledge discovery (3rd ed).
Potomac, MD: Two Crows Corp.
4. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in
knowledge discovery & data mining. Cambridge, MA: MIT Press.
5. Han, J., Kamber, M. (2000). Data mining: Concepts and Techniques. New York: MorganKaufman.
6. Haykin, S. (1994) Neural Networks. A Comprehensive Foundation. Macmillan, New York,
NY.
7. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning :
Data mining, inference, and prediction. New York: Springer.
8. John McCarthy(2007) What Is Artificial Intelligence. Available at: http://wwwformal.stanford.edu/jmc/whatisai/node1.html (Accessed: 23rd February 2012)
9. Pregibon, D. (1997). Data Mining. Statistical Computing and Graphics, 7, 8.
10. Statistical Data Mining (2002) Available at :
http://www.stats.ox.ac.uk/pub/bdr/SDM2002/DM2002.pdf (Accessed:24th February
2012)
11. What Is statsoft Solution for marketing (2011) Available at:
http://www.statsoft.com/solutions/marketing/ (Accessed: 23rd February 2012)
12. Weiss, S. M., & Indurkhya, N. (1997). Predictive data mining: A practical guide. New
York: Morgan-Kaufman.
13. Westphal, C., Blaxton, T. (1998). Data mining solutions. New York: Wiley.
14. Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan-Kaufmann.