Linear Clustering Algorithm BY Horne Ken & Khan Farhana & Padubidri Shweta

2 Overview Introduction Data Preprocessing Data Mining Data Visualization Experiment Conclusion

3 Responsibility Data Preprocessing : Farhana & Ken Data Mining : Ken Data Visualization: Shweta

4 Overview A Linear Clustering Algorithm Applications 1. Feature selection – Choose features based on information gain 2. Discretization – Partition based on data set characteristics

5 Data Preprocessing Data Ferret(Federated Electronic Research,Review,Extraction & Tabulation Tool) Install the software Web-version

6 Data Pre-processing : Step Extracted data from CPS (Current Population Survey) Pre-processing Number of features 43 Year ,000/month rows over 50 states After preprocessing 23 Normalization

Data Mining Algorithm Choose an ordinal attribute (X) Order data points based on attribute List potential partition points (between successive values of X) For each potential partition point P Calculate distance of data points where X P Results Can partition data points Order data points by information gain

Data Mining Test dataset

Data Mining Test dataset 2

10 Experimental Setup Environment 1. Data Ferret : Data Pre-processing 2. Java Platform : Implement the Data Mining Algorithm 3. Data Visualization 1. Google App Engine Datastore API Python, javascript and Django Framework 2. Google Chart API Hardware: Windows XP laptop Core GHz 2.00 GB RAM (that hurt)

11 Visualization Demo Link for the web-site

12 Conclusions Preliminary results are encouraging Discretization was successful Lessons learnt and future work Comparison with other methods on well known datasets Evaluate performance in feature selection OPTIMIZE Don't pick a novel dataset & novel algorithm at the same time

Thank you Questions