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Showing posts from September, 2012

An Introduction to Text Analytics

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Text analytics, sometimes alternately referred to as text data mining or text mining, refers to the process of deriving high-quality information from text.  High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.  Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output.  Typical text mining tasks include text categorization, text clustering, concept / entity extraction, production of granular taxonomies, sentiment analysis, document summarization and entity relation modeling (i.e., learning relations between named entities).  The overarching goal is, essentially, to turn text into data for analysis via application of natural language processing (NLP) and analytical methods.  A typical application is to scan a set of documents written in a natural language and either model the document…

Using decision trees in evidence based medicine

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In today's post, we explore the use of decision trees in evidence based medicine.  In 1996 David Sackett wrote that "Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients" [Source: Wikipedia].
For our analysis, we start with a data set which contains data about a number of patients all of whom suffered from the same illness.  Each of these patients responded well to one of five medications.  We will use a decision tree to understand what factors in each patients history led to them responding well to one specific medication over the others.  We will then use our findings to generate a set of evidence based rules or policies that can be followed by doctors to treat this illness in future patients.  As part of our analysis, we will also explore how to interpret decision trees.
Let us first look at our data set:

As can be seen, the data set contains information about the age a…

Numeric Measures for Association Rules

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In today's post, we dive into understanding Association Rules for Market Basket Analysis and discuss three numeric measures that should be considered before deciding to act on / make a business decision based on associations that have been observed in the data: (1) Support (2) Confidence and (3) Lift.

Association rules are typically written in the format:

Left hand side Implies Right hand side

The left hand side is referred to as the Antecedent and the right hand side is the Consequent.  The Antecedent means a thing that logically precedes another while a Consequent means a thing that follows as a result.  For example, in the association rule:

{Butter, Eggs} Implies {Bread}

Butter and eggs are the Antecedent while Bread is the Consequent.  What this rule means that if you were to pick a shopping cart at random and find butter and eggs in there, there is a chance that you are also likely to find bread.

The numeric measures mentioned above (Support, Confidence and Lift) are used to…