1. Enkh-Amgalan Baatarjav Jedsada Chartree Thiraphat Meesumrarn University of North Texas

2.  Evolution of Communication  Online Social Networking (OSN)  Architecture  Profile feature  Profile Analysis  Similarity inference  Clustering coefficient  Decision tree  Conclusion  Traditional medium of communication  Mail, telephone, fax, E- mail, etc.  Key to successful communication  Sharing common value

3.  User-driven content  Overwhelming number of groups  Finding suitable groups  Sharing a common value  Improving online social network

4.  Profile feature extraction  Classification engine  Clustering  Building decision tree  Group recommendation

5.  Group profile defined by profile features of users  Time Zone- Age  Gender- Relationship Status  Political View - Activities  Interest- Music  TV shows - Movies  Books- Affiliations  Note counts - Wall counts  Number of Fiends

6. SubtypeSizeDescription G1Friends12Friends group for one is going abroad G2Politic169Campaign for running student body G3Languages10Spanish learners G4Beliefs & causes46Campaign for homecoming king and queen G5Beauty12Wearing same pants everyday G6Beliefs & causes41Friends group G7Food & Drink57Lovers of Asian food restaurant G8Religion/Spirituality42Learning about God G9Age22Friends group G10Activities40People who play clarinets G11Sexuality319Against gay marriage G12Beliefs & causes86Friends group G13Sexuality36People who thinks fishnet is fetish G14Activities179People who dislike early morning classes G15Politics195Group for democrats G16Hobbies & Crafts33People who enjoys Half-Life (PC game) G17Politics281Not a Bush fan

7.  Hierarchical clustering  Normalizing data [0, 1]  Computing distance matrix to calculate similarity among all pairs of members (a)  Finding average distance between all pairs in given two clusters s and r (a) (b)

8. - R i is the normalized Euclidean distance from the center of member i - N k is the normalized number of members within distance k from the center

9.  Decision tree algorithm, based on binary recursive partitioning  Splitting rules  Gini, Twoing, Deviance  Tree optimization  Cross-validation (computation intense)

10.  Fair representation of group profile  Groups must have at least 10 members  Reduction  Users from 1,580 to 1,023  Group from 17 to 7 Group Size 1 274 2 226 3 159 4 151 5 133 6 67 7 13

11.  Data set  Training: 75%  Testing: 25%  Accuracy calculation  25 fold test  Accuracy  27%

14.  Feature score calculation  Using group profile: FSGP  Using group closeness: FSGC  Combination of FSGP and FSGC: FSPC

18. Feature Score Calculation Accuracy (%) Group–Profile Feature 24.47 STD of means 25.04 Mean of STDs 21.75

19.  Improving QoS of Online Social Networking  Architecture  Hierarchical clustering  Threshold value to reduce noise  Decision tree  Result poor performance cause  Decision tree: decision boundaries || to coord.  Data overlapping  More work on data cleaning  Feature reduction  From 12 to 2