1. Data Mining Durga Kumar

2. Internet Advertisements Data Set http://archive.ics.uci.edu/ml/datasets/Internet+Adverti sements http://archive.ics.uci.edu/ml/datasets/Internet+Adverti sements This dataset represents a set of possible advertisements on Internet pages. The dataset includes the geometry of the image (if available) as well as phrases occurring in the URL, the image's URL and alt text, the anchor text, and words occurring near the anchor text. Number of Instances: 3279 (2821 nonads, 458 ads) Number of Attributes: 1558 (3 continous; others binary)

3. Attribute description height: continuous. | possibly missing width: continuous. | possibly missing aratio: continuous. | possibly missing local: 0,1. url*images+buttons: 0,1.... origurl*labyrinth: 0,1.... ancurl*search+direct: 0,1.... alt*your: 0,1.... caption*and: 0,1. …

4. What to mine Choose Best possible attribute for Classification Classify as ads or non ads Finding the most weighted attribute Develop an algorithm for best possible feature set given any data set for classification with base set being this data set

5. Million songs Data Set http://labrosa.ee.columbia.edu/millionsong/ The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. The dataset does not include any audio, only the derived features. The Echo Nest

6. Attributes http://labrosa.ee.columbia.edu/millionsong/p ages/field-list http://labrosa.ee.columbia.edu/millionsong/p ages/field-list Fields of interest artist familiarity, bars start, beats start, loudness,tempo

7. What to extract Artist musical behavior Song classification Music Learners guide