Introduction to Decision Trees - Data Mining with Decision Trees: Theory and Applications - page 2

This service can help in epidemiological studies by aggregating certain
search terms that are found to be good indicators of the investigated disease.
For example, Ginsberg et al . (2008) used search engine query data to detect
influenza epidemics. However, a pattern forms when all the flu-related
phrases are accumulated. An analysis of these various searches reveals that
many search terms associated with flu tend to be popular exactly when flu
season is happening.
Many people struggle with the question: What differentiates data
science from statistics and consequently, what distinguishes data scientist
from statistician? Data science is a holistic approach in the sense that
it supports the entire process including data sensing and collection, data
storing, data processing and feature extraction, data mining and knowledge
discovery. As such, the field of data science incorporates theories and meth-
ods from various fields including statistics, mathematics, computer science
and particularly, its sub-domains: Artificial Intelligence and information
technology.
1.2 Data Mining
Data mining is a term coined to describe the process of shifting through
large databases in search of interesting and previously unknown patterns.
The accessibility and abundance of data today makes data mining a
matter of considerable importance and necessity. The field of data mining
provides the techniques and tools by which large quantities of data can
be automatically analyzed. Data mining is a part of the overall process
of Knowledge Discovery in Databases (KDD) defined below. Some of the
researchers consider the term “Data Mining” as misleading, and prefer the
term “Knowledge Mining” as it provides a better analogy to gold mining
[ Klosgen and Zytkow (2002) ] .
Most of the data mining techniques are based on inductive learning
[ Mitchell (1997) ] , where a model is constructed explicitly or implicitly by
generalizing from a sucient number of training examples. The underlying
assumption of the inductive approach is that the trained model is applicable
to future unseen examples. Strictly speaking, any form of inference in which
the conclusions are not deductively implied by the premises can be thought
of as an induction.
Traditionally, data collection was regarded as one of the most important
stages in data analysis. An analyst (e.g. a statistician or data scientist)
would use the available domain knowledge to select the variables that were