Day 3 - Team I

DAY 3_TEAM I_ (PRISKILINA BASUMATARI)

There are 2 type of clustering

1) Hierarchical

2) Non-hierarchical

Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. 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.

Non-hierarchical cluster analysis forms a grouping of a set of units, into a pre-determined number of groups, using an iterative algorithm that optimizes a chosen criterion.

K-means concept:

·         K-mean is used to take multiple clusters

·         It performs a non-hierarchical divisive cluster analysis on input data.

·         Have several features that distinguish it from the more common hierarchical clustering techniques.

·         executes a variance minimizing non-hierarchical cluster analysis

Dendogram:

It is a branching diagram representing a hierarchy of categories based on degree of similarity or number of shared characteristics. It is used to combine the clusters.

OLAP: online analytical processing:

OLAP cube is the representation of the data in a meaningful way to study and analyze. On-Line Analytical Processing (OLAP) is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.

OLAP functionality is characterized by dynamic multi-dimensional  analysis of consolidated enterprise data supporting end user analytical and navigational activities including:

• calculations and modelling applied across dimensions, through hierarchies and/or across members
• trend analysis over sequential time periods
• slicing- subsets for on-screen viewing
• drill-down - to deeper levels of consolidation
• reach- through- to underlying detail data
• rotation- to new dimensional comparisons in the viewing area

OLAP is implemented in a multi-user client/server mode and offers consistently rapid response to queries, regardless of database size and complexity. OLAP helps the user synthesize enterprise information through comparative, personalized viewing, as well as through analysis of historical and projected data in various "what-if" data model scenarios. This is achieved through use of an OLAP Server.

File Used: Cell_Inter.sav

                                                                                              OLAP Cubes

Gender of respondent: Total

Name of current service provider: Total

Connection Type: Total

OLAP Cube for Overall Cell bills

	Sum	N	Mean	Std. Deviation	% of Total Sum	% of Total N
Usage period In Months	2569	206	12.47	9.084	100.0%	100.0%
Monthly expenditure on phone	72633.00	206	352.5874	184.64170	100.0%	100.0%
Fixed component of bill	9914.00	206	48.1262	19.59825	100.0%	100.0%
Voice calls bill	9985.00	206	48.4709	28.83031	100.0%	100.0%
SMS bill	5519.00	206	26.7913	17.64308	100.0%	100.0%
Other charges	1147.00	206	5.5680	11.18940	100.0%	100.0%
Special Package	995	206	4.83	2.049	100.0%	100.0%
Games	234	206	1.14	.344	100.0%	100.0%
Other	402	206	1.95	.215	100.0%	100.0%