Download The keypoint of neural network is it has the predicting power rather

The keypoint of neural network is it has the predicting power rather than t
explanation power of the complex patterns. See Figure B. 5. for Neural Network Mode
Neural network learn to predict outputs by training themselves on sample data. Th
network reads the sample data and iteratively adjusts the network's weights to produ
optimum predictions. Then the neural network applies its knowledge to data being mine
For more details on neural network please refer to the article by Claude B. Cruz, entitle
"Understanding Neural Networks" and many of such offing are available.
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NEURAL NETWORK MODEL
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Oil Prod.
>
Gas Prod.
Software
INPUT LAYER
HIDDEN LAYER
OUTPUT LAYER
Figure B. 5. Neural Network Model
APPENDIX -C
MORE DETAILS ON INTEGRATED METHODOLOGY
1. SETTING GOALS
Though our thesis is nothing to do with
the methodology of setting goals,
since the effective strategic plans are only possible if we carefully devise our goa
Otherwise our strategic plans which are based on the goals we set will not be beneficial
all in the organization. If this is the case, then our integrated approach will fail as well.
avoid such a disastrous result, we need to have goals setting properly implanted in o
organization. And it is a must! Setting goals is the foundation where we can proce
further for making our strategic plans in action.
Without proper goals setting, there will be very slim chance to have an effecti
strategic plans. There is the main reason why this process is included in the integrat
methodology. The pre-conditions are necessary so that before we are doing any go
setting task we have been equipped ourselves with clear vision, mission and drivi
forces to purport our efforts in order to achieve the goals. Without these pre-conditio
our goals setting might not be in alignment with the organization's vision and missio
The post-conditions are necessary to make sure that we are not only setting goals witho
considering the implication of the goals. With post-conditions labeled, there will be
yardstick to cross-check whether the goals satisfy our criteria. The above pre-conditio
and post-conditions are of the knowledge of the writer after several refinement process
there are no restrictions to add or remove the pre-conditions and the post-conditions
The process of goals setting
is try to set goals that specific, measurab
achievable, realistic, timely, and also agreeable to management.
2. GOALS AND PROBLEMS ANALYSIS
After arriving at SMART-based goals and acceptable by management, we do n
stop there, but we need to continue to refine the goals to ensure all the problems related
goals have been considered and analyzed in shed-light of the goals. With this furth
refinement, the goals will become more pragmatic and practical within the organizatio
In additions, problems that might arise due to the goals have been anticipated.
There are two types of yardsticks that we can use for analyzing our goals a
problems: financial yardstick and resources yardstick. First we need to analyze wheth
the goals are financial related. It means whether the goal will require big finances to
propelled. So often we neglect this financial yardstick, the result is clear, our strateg
plan will do no good to the organization. The same applies to resources yardstick, witho
considering our resources, we might trap ourselves into a fatal result, that is lacki
resources both humans and non-humans.
3. CRITICAL SUCCESS FACTORS ANALYSIS
After we get our goal to be SMART-based, practical, pragmatic, and ev
financial and resources-sound, we need to partition the goal into smaller goals that can
further used in our PECMA model as a subject/result/goal variable. If our goals set is t
wide, we also can go back to our setting goals step by using CSF produced in this stage
our renewed goals. The process continue until we think the CSF are small enough to
used as goal variable in our intelligent data mining processes. There is no specific rule
doing this, we the rule of thumb is as long as the CSF can be contributed to the PECM
model as the variable, then we are in business. The results after applying this Criti
Success Factors Analysis are factors that must go right if we would like to see t
organization to achieve its goals. These factors then are passed-on to the PECMA mo
and also to the interpret and associate steps in data mining.
It is very clear that as the refined goal has been set in the previous step, we need
partition the goal into smaller goals or the factors that must go right. Without maki
right of this factor, our goal can be thwarted and not achievable.
4. DATA COLLECTION
In our integrated methodology, data collection is the first important step we ne
to carry out in Intelligent Data Mining. Only with this step, we can warrant that the d
have been wisely collected and have satisfied our required format. Without these po
conditions, we might have data availability and data accessibility of no point to kn
where they are. Or if by any chance, the data have not been digitized, then the on
possibility to use our integrated model is by digitize them electronically, this could be
simple as typing-in the data into computer.
5. DATA COMBINATION
After knowing where our data are, we need to ensure that those data are read
accessible at one-go if needed. There is also a possibility that we combine more than o
The process of goals setting
is try to set goals that specific, measurab
databases into one integrated databases for our data mining purposes. There are spec
6.1. PECMA MODEL: THE PRE-PROCESSING STAGE
As we know that the final output of our complete model is an effective strateg
writing. We see this step is mecessary to avoid the difficult access at the later stage.
6. DATA DISTILLATION AND MODELING
This stage is what we call "PECMA MODEL", it stands for Pre-processin
Exploration, Consolidation, Modeling, and Assessment. And it is the core of Intellige
Data Mining activities. It is also a state where data mining is integrated with strateg
planning by sharing information regarding the critical success factors generated fro
strategic planning activity, i.e. during Critical Success Factors Analysis stage. This sta
is necessary to transfonn our data into representative and strategic informatio
statistically meaningful information, and even the models for data evaluation.
In order to see how our case study can fit into this model, we are ready to explo
into the PECMA meticulously. PECMA model is not a stand-alone model but only wi
contributions from strategic planning information such as CSFs, this model can functi
properly and achieve our goal of delivering more effective planning than the one witho
using this integrated model. The know-how of the data behavior is necessary to spawn t
legitimate model for the pertinent data. After this stage, we are having representative a
statistically meaningful information, and also the accompanied model(s).
chapters in some databases books that dedicate this process of how of making couple
databases into one integrated database. The details of this is beyond the discussion of th
planning, this can not be realized if our data analysis is based on some data imperfectio
In most cases, imperfections with the data are not readily noticed until the data analy
starts. As our purpose is to discover knowledge/relationships that will be used to thr
light in strategic planning, problems with data may prevent this. We would agree that t
reliability of the portion of the knowledge/relationships that are generated through o
methodology depends ve1y much on data we have,
if we
have some very biased data,
also means we will establish a very misleading model, in turn will impact our strate
planning which we hope to be more effective but become disastrous.
According to Richard Weber (Weber, 1996, Internet), data pre-processing may
performed on the data for the following reasons:
•
solving data problems that may prevent us from performing any type
analysis on data
•
understanding the nature of the data and performing a more meaningful d
analysis
•
extracting more meaningful knowledge/relationships from a given set of dat
DATA PROBLEMS IN REAL WORLD
t'
•'
Too Much Data
Too Little Data
-CorruJJt and noisy data
-Irrelevant duta
-Missing Attributes
-Very large data size
-Small amount of data
-Missing Attributes Value
'
.
Fractured Data
Incompatible data
-Multiple data sources
-Data from multiple levels
of granularity
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Data Transformation
Information Gathering
-Data Filtering
-Data visualization
-Data Ordering
-Data elimination
-Data selection
-Data sampling
-Data Editing
-Noise Modeling
New Information Generation
-Data Engineering
-Data Fusion
-Time series analysis
.Simulation
-Dimensional annlysis
DATA PREPROCESSING TECHNIQUES
Figure C.1. Data Problems and Data Preprocessing Techniques
Data problems can be classified into three groups of: too much data, too little dat
and fractured data. Figure C.l
shows problems with data and data pre-processi
techniques available to tackle such a problem. That is the main reason we incorpora
data pre-processing into our methodology to handle those problesms. Fayyad, Piatetsk
Shapiro, and Smyth (Fayyad, 1996, pp. 1-34) emphasize use of data pre-processi
techniques as an essential part of any knowledge discovery from a database project. T
techniques listed on Figure 4.1 can be read in more details in some advanced statistic
books, papers and journals. There are also some software available in the market pla
that can do data pre-processing for us automatically, such as data sampling, da
visualization, data filtering, data ordering, data elimination, data fusion, dimension
analysis, and many others. And there are some data mining software that have includ
data sampling techniques inherently in the software such as DB-Miner, etc.
Of course, there is no guaranteed that after pre-processing, our data are I 00% f
of errors, but at least the data after pre-processing are expected to behave more reliab
thus allowing us to construct the accurate models.
6. 2. PECMA MODEL: THE EXPLORATION STAGE
We can say basically that during the exploration stage we are turning our p
processed data into insights. Those insights are mainly based on the trends, rules, a
even anomalies constructed during the exploration stage.
Even the name data mining is very strongly shaped by information discove
features. Information discovery is just like the vein in data mining processes since on
with information discovery the meaning of data mining can be further enhanced. Witho
information discovery, the process of data mining will not contribute much to strate
planning. Infonnation discovery is also can be referred as "the process of generati
information from the massive available data".
6. 3. PECMA MODEL: THE CONSOLIDATION STAGE
The consolidation stage is the stage where we confederate the variables, rul
anomalies, and trends. The consolidation is necessary because only those variables, rul
anomalies or trends that we think having
contributions to strategic planning will
grasped, and since there might be many of those, we need to consolidate them.
6. 4. PECMA MODEL: THE MODELING STAGE
It is quite clear that during this stage we construct models that accommodate o
data. This model can be used to check again our data. By doing this, we will have mo
consistent findings. Say if our model can behave accurately in almost all cases we test
means that we have constructed a good model, and if we have a good model, it means
are in good shape to accept our findings, thus allowing us to devise effective strate
plans based on those findings. In additions, we are also able to use the model to pred
the outcomes if we know the pertinent information. Since with this model, we are putti
ourselves to predict some outcome, at the same time we can devise strategy that
capable of anticipating the outcomes.
As discussed in the integrated methodology that we can construct many differe
models from our findings and variables, such as the simplest If-Then models to the m
complex neural network models. In our case study, we will give construct two differe
models, one from If-Then Model and the other from Neural Network Model. For our tr
based model, please refer to our "The Design of The Methodology" section whi
explains such a model together with the example pictorially.
6. 5. PECMA MODEL: THE ASSESSMENT STAGE
We might have developed many models during this stage, but we need to t
every model we have to ensure the validity with the incorporated data. Otherwise t
model will be meaningless. During this stage, we evaluate the models, and if the model
not accurate we might go back to our modeling stage, and restructure our modeling. The
is no clear cut of how to assess the models, but we need to bear in mind that o