The day started with another practical
example, the data given data was about the usage of different features of
mobile phone such as (SMS, Games, and Alarm).
We used hierarchical clustering get the
initial interpretation of the given data. Strategies for hierarchical
clustering generally fall into two types:
- Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
- Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
The
critical element of hierarchical clustering is the dendrogram, The results of
hierarchical clustering are usually presented in a dendrogram. It also helps us
decide how many clusters are there in the clustering.
In
general we use hierarchical clustering when the numbers of elements to be
grouped are < 50. If there are large number of elements than it will result
in a huge dendrogram, which is very difficult to analyze. In the given data we
have 206 cases and 45 variables, so we use variables for
clustering.
So far we have only looked at clustering
cases. We have also only looked at dealing with interval data. So, while we
have a brief look at clustering variables, we also use a different kind of
data, namely binary data. Just a look at a table which show if one uses a
feature or not does not convey much of impression of how the variables may be
related, if they are.
To see is cluster analysis some light on this we using distance measures on this binary data. There are two methods the “JACCARD” and “EUCLIDEAN”.
An OLAP cube is an array of data
that is understood in terms of its 0 or more dimensions. OLAP is an
acronym for online analytical processing. Online analytical processing (OLAP)
is a technique for quickly analyzing a measure, e.g. profit margin, by multiple
categories or dimensions, e.g. customer, region, fiscal period and product
line. Typically the end user software has capabilities to drag
categories to rows and columns and aggregate the measure at each
intersection of a row and column (often called a cross tab report).
This
is similar to the familiar spreadsheet format. This numeric format can
usually also be represented in the form of a chart or graph. The real
power of OLAP is the ability to drill down on a category to see more
details. For example, you might drill down on a state to see details by
city. So here we can use the OLAP cube various things such as:
·
Which age group spends more on
monthly basis;
·
If educated people spend more
than uneducated on bills
·
Which gender spends more on
monthly basis etc.
Some via various combination and calibrations(known as story telling) we drive a relation and develop marketing mix, which is the real aim of making all this research analysis.
BY - Shishir Borkar
Team F
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