K means Clustering ( Weka )

Data Mining Technique K means Clustering (Weka) Recommends similar cities Recommendations are based on user input and 75 attributes Number of clusters are determined by the sum of squares (minimum distance) 1

Business Plan Increased Revenue 2

Competitor Analysis 3

NOVELTY Recommender System In Built Weka for dynamic clustering Google Map for nearby cities Geocoding for calculating shortest nearby distances All in one place needed for relocation Real Estate listings, Jobs, cost calculator Dynamic population of Map Xml population of clustered cities dynamically 2

Future Plan Partner with Expedia.com Airline tickets Hotel rentals Rental car service 4

Work Allocation Ambarish Weatherbug APICareer Builder API Google Map API Database Survey Scott Flash Map Fizber API Fed Spending API Recommender System Zillow API Database Dan Web Front Widgets Email Contract Form YouTube API Chat Room Samarth Database Web Front Cost Calculator Real Estate API Distance Calculator Flickr API 5

K means Clustering ( Weka )

K means Clustering ( Weka )

Presentation Transcript

k -means Clustering

K-means Clustering

K-means Clustering

Canopy Clustering and K-Means Clustering

K-MEANS CLUSTERING

K-Means Clustering

K-means clustering

Combinatorial clustering algorithms. Example: K-means clustering

K-means Clustering

Initial K-Means Clustering :

Clustering ( Season 1 ) K-Means

K-means Clustering

Determining the ‘k’ in k-Means Clustering

K-means Clustering

Clustering Beyond K -means

Clustering: K-Means

Rek-means A k-means Based Clustering Algorithm

K-means clustering

Categorical K-means Clustering Algorithm