AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

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KNOWLEDGE ENGINEERING AND DATA MINING
Traditionally, data has been analysed with user-driven techniques, where a
user formulates a hypothesis and then tests and validates it with the available
data. A query tool is, in fact, one such technique. However, as we already
know, the success of a query tool in discovering new knowledge is largely based
on the user’s ability to hypothesise, or in other words, on the user’s hunch.
Moreover, even experts are not capable of correlating more than three or, at
best, four variables, while in reality, a data warehouse may include dozens
of variables, and there may be hundreds of complex relationships among
these variables.
Can we use statistics to make sense of the data?
Statistics is the science of collecting, organising and utilising numerical data. It
gives us general information about data: the average and median values,
distribution of values, and observed errors. Regression analysis – one of the most
popular techniques for data analysis – is used to interpolate and extrapolate
observed data.
Statistics is useful in analysing numerical data, but it does not solve data
mining problems, such as discovering meaningful patterns and rules in large
quantities of data.
What are data mining tools?
Data mining is based on intelligent technologies already discussed in this book.
It often applies such tools as neural networks and neuro-fuzzy systems. However,
the most popular tool used for data mining is a decision tree.
What is a decision tree?
A decision tree can be defined as a map of the reasoning process. It describes a
data set by a tree-like structure. Decision trees are particularly good at solving
classification problems.
Figure 9.44 shows a decision tree for identifying households that are
likely to respond to the promotion of a new consumer product, such as a
new banking service. Typically, this task is performed by determining the
demographic characteristics of the households that responded to the promotion
of a similar product in the past. Households are described by their ownership,
income, type of bank accounts, etc. One field in the database (named
Household) shows whether a household responded to the previous promotion
campaign.
A decision tree consists of nodes, branches and leaves. In Figure 9.44, each box
represents a node. The top node is called the root node. The tree always starts
from the root node and grows down by splitting the data at each level into new
nodes. The root node contains the entire data set (all data records), and child
nodes hold respective subsets of that set. All nodes are connected by branches.
Nodes that are at the end of branches are called terminal nodes, orleaves.
Each node contains information about the total number of data records at
that node, and the distribution of values of the dependent variable.