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Web Mining

Outline

• Hype of the web• Difficulties with web • Web Mining• Advantages / Disadvantages• Categories of Web Mining• Web Usage Mining

Hype Of The Web

§ Information§ Services§ E-mail§ Business§ Communities

Difficulties With Web

§ Very Huge Information§ Semi-Structured Data§ Redundant Data§ Web is noisy§ Customer Behaviour

Web Mining

Process of discovering useful and unknown information or knowledge from the web data.

Web Mining - Sub Tasks

• Resource Finding• Information Selection• Information Preprocessing• Data Mining Techniques• Analysis

Benefits

• Business Organization• Banking Sector• Customer Satisfaction• Researchers• Society

Drawbacks

• Privacy Issues• Security Issues• Mis-Use of Information• Sometimes Expensive

Categories

Web Usage Mining

The Process of Automatic discovering patterns and profile of users interacting with a web site.

Web Usage Mining Model

Data Cleaning: Clean the raw data,

-Missing value-Redundant data

-Outliers deletion

Trasaction Derivation:

-according to individual user transaction

Data Integration:

- Combines data from multiple sources into a data store

Transformation:

- The data are transformed into appropriate forms for mining such as generalization and normalization

Pattern Discovery

Association Rule:

• X == > Y (support, confidence)• 60% of clients who accessed /products/, also

accessed /products/software/webminer.htm

Sequential Pattern:

• Discovery of frequently occurring ordered events or subsequences as patterns.

Clustering

process of grouping a set of objects into classes of similar objects

Classification

process of finding a model that describes and distinguishes data classes or concepts

K-means Algorithm

Used for clustering, where each cluster’s center is represented by the mean value of the objects in the cluster.

Input: k: the number of clusters, D: a data set containing n objects.Output: A set of k clusters.

Steps:

(1) randomly choose k objects from D as the initial cluster centers;

(2) repeat

(3) (re)assign each object to the cluster to which the object is the most similar , based on the mean value of the objects in the cluster;

(4) update the cluster means, i.e., calculate the mean value of the objects for each cluster;

(5) until no change;

K-Medoids Algorithm

-To over come the limitations found in k-means algorithm -Cluster is represented by the cost value of the objects in the cluster.

Input:

k: the number of clusters,

D: a data set containing n objects.

Output:

A set of k clusters.

Steps:

(1) randomly choose k objects in D as the initial representative objects or seeds;

(2) repeat

(3) assign each remaining object to the cluster with the nearest representative object;

(4) randomly select a nonrepresentative object, orandom;

(5) compute the total cost, S, of swapping representative object, oj, with orandom;

(6) if S < 0 then swap oj with orandom to form the new set of k representative objects;

(7) until no change;

P : non represntative objectOj : current representative objectOi : another representative objectOrandom : good replacement object (non represetative object)

Thank You