Geo-word Centric Association Rule Mining

1
Geo-word Centric Association Rule Mining
2
About Me

• Fahad Al-Emam
• Bachelors of CSE from MSU (04)
• Masters student in the College of Computing
  specializing in Software Engineering
• Graduating this Fall !

3
Summary

• Humans perceives a location in the verbal form,
  such as address, landmark and other well known
  terms.
• Most user interface of location based services
  provide input methods based on those verbal forms
  because they are the most intuitive and easiest
  to use for human users.
• This verbal form is referred to as geo-word.
• The main focus of the paper describes using
  geo-words in Association Rule Mining.

4
Background Terminology

• Association rule mining finds all the rules
  existing in the database that satisfy a minimum
  support and confidence constraint.
• Let I i1 i2 i3 i4 in be a set of
  itemsLet T t1, t2 .. tn be a set of
  TransactionsA Rule is defined as an implication
  of the formX ? Y where X, Y ? I and X n Y
  0
• Support of an itemset is proportion of
  Transactions that contain it.
• Confidence of a rule sup(x n y) / sup(x)

5
Geo-Words Geo-Tuples

• Geo-word a proper noun which represents a
  location related human understandable concept. An
  address is a typical example of geo-word as well
  as the name of a landmark.
• A non-proper noun such as "mountain" is also not
  considered as a geo-word. Neither is IP or
  lat/lon
• Geo-word tuple is a set of items which contains
  at least one geo-word
• TG ( g, ik .. in ) where i can be keyword, area
  status or even temperature. (1)

6
Geo-word Centric Association Rule

• A geo-word centric association rule
• RG Xg ? Yg
• Geo-word association rule
• The rule format explicitly contains at least a
  geo-word either on the left side of the rule X
  or the right side of the rule Y as formulated
• 
  (2)
• Co-location association rule
• The rule does not contain geo-word but it is
  generated from geo-word tuples and the geo-word
  becomes a constraint to the rule. All of the
  items considered in the rule come from geo-word
  tuples of the same geo-word. In other word, when
  the geo-word tuples satisfy (1) then the itemsets
  are related to a specific geo-word as formulated
• 
  
  (3)

7
Geo-word Centric Assc. Rule Mining

• Over all Process of geo-word centric association
  rule mining. Some geo-word specific processes
  include geo-word cleaning, geo-word scaling and
  geo-word specific interestingness measures. The
  association rule mining itself can use any
  available classic mining algorithms

8
Geo-Word Tuple Processing

• There are three type of Geo-word tuple processing
  which results in intermediate data
  representations that become itemsets for
  association rule mining
• Geo-word tuple This basic methodology only
  considers the items in the geoword tuples. This
  processing method will generate geo-word
  association rules with the support equal to the
  number of tuples in the itemset.
• Session When tuples contain user ID and
  timestamp they can be grouped into sessions based
  on the unique users and a specified time
  interval. This processing method also generates
  geo-word association rules but the support of the
  rule is the number of sessions instead of the
  number of the tuples.
• Co-location tuple Co-location tuple contains
  only the items which co-occur with a geo-world in
  the tuple. Since co-location tuples do not
  necessarily contain any geo-word, the mining
  results can be regarded as co-location
  association rules.

9
Geo-Word Tuple Processing (cont.)

• Here is an example of the processing preformed on
  an access log
• Geo-word tuples are grouped
• Session are grouped by time ID
• Co-location are grouped by geo-word that relates
  to them

10
Experiment

• Target Log Data Access log data used for mining
  is from an online commercial location based
  search service (Japanese yellowpages) each line
  of the log is called request which includes a
  geo-word, free-words, and a time stamp. Data
  collected over 3 months.
• Data Preprocessing Each request of log data is
  abstracted by only picking items required for
  mining. After removing abnormal requests, log
  data is divided into sessions.
• Statistics of the Target Data Original raw data
  consisted of 500 million accesses and its size is
  150 Giga bytes. After the preprocessing, it was
  converted into 14 million accesses and 800 Mega
  bytes data.
• Association rules Generated from 14 million
  requests using the method described in Processing
  slide. The minimum support for the rules is
  0.000002. Each rule includes geo-words, and
  freewords.

11
Experiment (Cont.)
12
Experiment (Cont.)
13
Experiment (Cont.)
14
Experiment (Cont.)
15
LIFT

• Observation In some cases, there were too many
  rules generated for a given word. For example,
  there are more than 1000 kinds of association
  rules which hotel is on the left part in our
  generated geo-word association rules. Therefore,
  we need a metric that can measure useful and
  interesting rules one known metric is called
  lift. The lift is a method that can measure
  how interesting/Unique a rule is, and a lift
  for rule R X ?Y is sup(X ?Y)/(Sup(X)Sup(Y))

16
LIFT
17
Entropy

• The geo-word for a requested word can have
  different significance based on the notion of
  local specialty and local commodity. If a
  user request is the word super market, he/she
  may wish to know how to find it in the vicinity
  since one can be found in many places. However,
  if the word is not so common such as kabuki
  theater, he/she may want to know the location
  regardless of proximity to the vicinity.
• Entropy Is a metric can identify the degree of
  local specialty of a given word. If we regard w a
  word as information source for g ?G , then
  Entropy H(w) -S N(g,w)log(N(g,w) / N(w))
• When a free-word w is requested, if the
  accompanying geo-word g is the same all the time,
  then entropy 0 . If g differs all the time then
  H(w) is maximum

18
Entropy (Cont.)
19
Conclusion

• Paper was extremely difficult to read.
• Discussion need more popular examples
• Didnt mention any actual mining algorithms used
  in research or in experiment.
• By the end of the 3rd page, all citations
  completed!
• Math presented without discussion.

20

• Q A