Web Analytics Understanding user behavior and ... - pace university

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D: Analysis of the user activity: The user keyword is known from the log files. If the keyword matches the information based on the page and there is considerable time spent, both by clicking or surfing ( reading on) then it is understood as a serious user. The number of clicks ( HTML pages visited) are counted, amount of time spend is also known. All these factors are taken into account and understood whether there was a keyword match, serious user or not. This is again tabulated to extract decision trees using attributes like keyword, destination (yes/no). 54
E: Creation of data sets for See5/C5.0 See5/C5.0 is used to extract patterns from the user behavior on the website. The patterns are extracted with website destination as key attribute. Keyword as attribute is also used. In this case, the analysis has been for all the users who have come to the root page of the csis. If the user is exiting the page immediately by not accessing any further information in just few seconds (in most cases its 2 sec, but the duration has varied from 1-4 secs), then the users destination has not been CSIS. Stages are nothing but the pages accessed by the user right immediately after the query in the search website. If the user just visits the root page then its just stage 1. If more pages have been visited by the user then accordingly stages have been given numbers. Stage 3 is given either number 1 or more to indicate the number of pages, he/she went to. 1. Data File for See5/C5.0: The below csis.data file is a partial data file from the original data file. csis.data pace university,1,1,1,yes. pace university,1,0,0,no. PACE,1,0,0,no. Pace University,1,0,0,no. health curriculums,1,0,0,no. 55
Page 1 and 2:
Web Analytics Understanding user be
Page 3 and 4: CONTENTS Acknowledgement………
Page 5 and 6: web site what he or she needs. But
Page 7 and 8: a decision tree to help a financial
Page 9 and 10: With data mining, a retailer could
Page 11 and 12: Occam's Razor (Specialized to Decis
Page 13 and 14: Pete Brown Tall Heavy No None John
Page 15 and 16: Attribute: Height Reference: positi
Page 17 and 18: D) Now let’s examine to see if th
Page 19 and 20: Similarly, we now choose another te
Page 21 and 22: See5/C5.0 See5/C5.0 is a sophistica
Page 23 and 24: Attribute Case 1 Case 2 Case 3 ....
Page 25 and 26: The file hypothyroid.names looks li
Page 27 and 28: Explicitly-defined attributes The n
Page 29 and 30: Attributes defined by formulas The
Page 31 and 32: the value of flight time is the num
Page 33 and 34: The main window of See5 has six but
Page 35 and 36: Many of the options have default va
Page 37 and 38: :...TT4 61: :...referral source in
Page 39 and 40: else if on thyroxine equals f then
Page 41 and 42: The Problem Server log files from C
Page 43 and 44: A: Server log files The following a
Page 45 and 46: 24.4.255.251 - - [02/Jul/2001:16:59
Page 47 and 48: 128.48.26.71 / "http://www.google.c
Page 49 and 50: w%5D=&wf%5B1%5D%5Br%5D=%2B&wf%5B1%5
Page 51 and 52: 200.205.16.130 - - [05/Jul/2001:19:
Page 53: 200.205.16.130 - - [05/Jul/2001:19:
Page 57 and 58: pace university,1,0,0,no. pace univ
Page 59 and 60: PACE,1,0,0,no. pace university,1,1,
Page 61 and 62: Time: 0.0 secs Explanation: -------
Page 63 and 64: No of users: 103 2.Pace university
Page 65 and 66: A Real Life Scenario Kat walks into
Page 67 and 68: DHTM can be used to create links ba
Page 69 and 70: A) Website: http://csis.pace.edu/~b
Page 71 and 72: As the page loads links based on th
Page 73 and 74: http Referrer: Null When the http r
Page 75 and 76: Case 1: When the user came to the w
Page 77 and 78: As the page loads links based on th
Page 79 and 80: Hence, the administrator tool gives
Page 81 and 82: Issues and Scope: Cleaning the 412M
Page 83: an understanding will lead to some
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