Post on 19-Dec-2015
2002.10.31 - SLIDE 1IS 202 – FALL 2002
Prof. Ray Larson & Prof. Marc Davis
UC Berkeley SIMS
Tuesday and Thursday 10:30 am - 12:00 pm
Fall 2002http://www.sims.berkeley.edu/academics/courses/is202/f02/
SIMS 202:
Information Organization
and Retrieval
Lecture 18: Vector Representation
2002.10.31 - SLIDE 2IS 202 – FALL 2002
Lecture Overview
• Review– Content Analysis– Statistical Properties of Text
• Zipf Distribution• Statistical Dependence
– Indexing and Inverted Files
• Vector Representation• Term Weights• Vector Matching• Clustering
Credit for some of the slides in this lecture goes to Marti Hearst
2002.10.31 - SLIDE 3IS 202 – FALL 2002
Lecture Overview
• Review– Content Analysis– Statistical Properties of Text
• Zipf Distribution• Statistical Dependence
– Indexing and Inverted Files
• Vector Representation• Term Weights• Vector Matching• Clustering
Credit for some of the slides in this lecture goes to Marti Hearst
2002.10.31 - SLIDE 4IS 202 – FALL 2002
Techniques for Content Analysis
• Statistical– Single Document– Full Collection
• Linguistic– Syntactic– Semantic– Pragmatic
• Knowledge-Based (Artificial Intelligence)
• Hybrid (Combinations)
2002.10.31 - SLIDE 5IS 202 – FALL 2002
Content Analysis Areas
How isthe text processed?Index
Pre-Process
Parse
Collections
Rank
Query
Text Input
How isthe queryconstructed?
InformationNeed
2002.10.31 - SLIDE 6
Document Processing Steps
From “Modern IR” Textbook
2002.10.31 - SLIDE 7IS 202 – FALL 2002
Errors Generated by Porter Stemmer
Too Aggressive Too Timid organization/ organ european/ europe
policy/ police cylinder/ cylindrical
execute/ executive create/ creation
arm/ army search/ searcher
From Krovetz ‘93
2002.10.31 - SLIDE 8IS 202 – FALL 2002
A Small Collection (Stems)Rank Freq Term1 37 system2 32 knowledg3 24 base4 20 problem5 18 abstract6 15 model7 15 languag8 15 implem9 13 reason10 13 inform11 11 expert12 11 analysi13 10 rule14 10 program15 10 oper16 10 evalu17 10 comput18 10 case19 9 gener20 9 form
150 2 enhanc151 2 energi152 2 emphasi153 2 detect154 2 desir155 2 date156 2 critic157 2 content158 2 consider159 2 concern160 2 compon161 2 compar162 2 commerci163 2 clause164 2 aspect165 2 area166 2 aim167 2 affect
2002.10.31 - SLIDE 9IS 202 – FALL 2002
The Corresponding Zipf Curve
Rank Freq1 37 system2 32 knowledg3 24 base4 20 problem5 18 abstract6 15 model7 15 languag8 15 implem9 13 reason10 13 inform11 11 expert12 11 analysi13 10 rule14 10 program15 10 oper16 10 evalu17 10 comput18 10 case19 9 gener20 9 form
2002.10.31 - SLIDE 10IS 202 – FALL 2002
Zipf Distribution
• The Important Points:– A few elements occur very frequently– A medium number of elements have medium
frequency– Many elements occur very infrequently
2002.10.31 - SLIDE 11
Zipf Distribution
Linear Scale Logarithmic Scale
2002.10.31 - SLIDE 12IS 202 – FALL 2002
Related Distributions/”Laws”
• Bradford’s Law of Scattering
• Lotka’s Law of Productivity
• De Solla Price’s Urn Model for “Cumulative Advantage Processes”
½ = 50% 2/3 = 66% ¾ = 75%Pick Pick
Replace +1 Replace +1
2002.10.31 - SLIDE 13IS 202 – FALL 2002
Frequent Words on the WWW• 65002930 the• 62789720 a• 60857930 to• 57248022 of• 54078359 and• 52928506 in• 50686940 s• 49986064 for• 45999001 on• 42205245 this• 41203451 is• 39779377 by• 35439894 with• 35284151 or• 34446866 at• 33528897 all• 31583607 are
• 30998255 from• 30755410 e• 30080013 you• 29669506 be• 29417504 that• 28542378 not• 28162417 an• 28110383 as• 28076530 home• 27650474 it• 27572533 i• 24548796 have• 24420453 if• 24376758 new• 24171603 t• 23951805 your• 23875218 page
• 22292805 about• 22265579 com• 22107392 information• 21647927 will• 21368265 can• 21367950 more• 21102223 has• 20621335 no• 19898015 other• 19689603 one• 19613061 c• 19394862 d• 19279458 m• 19199145 was• 19075253 copyright• 18636563 us
(see http://elib.cs.berkeley.edu/docfreq/docfreq.html)
2002.10.31 - SLIDE 14IS 202 – FALL 2002
Word Frequency vs. Resolving Power
The most frequent words are not the most descriptive
(from van Rijsbergen 79)
2002.10.31 - SLIDE 15IS 202 – FALL 2002
Statistical Independence
• Two events x and y are statistically independent if the product of the probabilities of their happening individually equals the probability of their happening together
),()()( yxPyPxP
2002.10.31 - SLIDE 16IS 202 – FALL 2002
Lexical Associations
• Subjects write first word that comes to mind– doctor/nurse; black/white (Palermo & Jenkins 64)
• Text Corpora can yield similar associations• One measure: Mutual Information (Church and
Hanks 89)
• If word occurrences were independent, the numerator and denominator would be equal (if measured across a large collection)
)(),(
),(log),( 2 yPxP
yxPyxI
2002.10.31 - SLIDE 17IS 202 – FALL 2002
Interesting Associations with “Doctor”
I(x,y) f(x,y) f(x) x f(y) y11.3 12 111 Honorary 621 Doctor
11.3 8 1105 Doctors 44 Dentists
10.7 30 1105 Doctors 241 Nurses
9.4 8 1105 Doctors 154 Treating
9.0 6 275 Examined 621 Doctor
8.9 11 1105 Doctors 317 Treat
8.7 25 621 Doctor 1407 Bills
AP Corpus, N=15 million, Church & Hanks 89
2002.10.31 - SLIDE 18IS 202 – FALL 2002
I(x,y) f(x,y) f(x) x f(y) y0.96 6 621 doctor 73785 with
0.95 41 284690 a 1105 doctors
0.93 12 84716 is 1105 doctors
These associations were likely to happen because the non-doctor words shown here are very common
and therefore likely to co-occur with any noun
Un-Interesting Associations with “Doctor”
AP Corpus, N=15 million, Church & Hanks 89
2002.10.31 - SLIDE 19IS 202 – FALL 2002
Content Analysis Summary
• Content Analysis: transforming raw text into more computationally useful forms
• Words in text collections exhibit interesting statistical properties– Word frequencies have a Zipf distribution– Word co-occurrences exhibit dependencies
• Text documents are transformed to vectors– Pre-processing includes tokenization, stemming,
collocations/phrases– Documents occupy multi-dimensional space
2002.10.31 - SLIDE 20IS 202 – FALL 2002
Inverted Indexes
• We have seen “Vector files” conceptually– An Inverted File is a vector file “inverted” so
that rows become columns and columns become rows
docs t1 t2 t3D1 1 0 1D2 1 0 0D3 0 1 1D4 1 0 0D5 1 1 1D6 1 1 0D7 0 1 0D8 0 1 0D9 0 0 1
D10 0 1 1
Terms D1 D2 D3 D4 D5 D6 D7 …
t1 1 1 0 1 1 1 0t2 0 0 1 0 1 1 1t3 1 0 1 0 1 0 0
2002.10.31 - SLIDE 21IS 202 – FALL 2002
How Inverted Files are Created
Dictionary PostingsTerm Doc # Freqa 2 1aid 1 1all 1 1and 2 1come 1 1country 1 1country 2 1dark 2 1for 1 1good 1 1in 2 1is 1 1it 2 1manor 2 1men 1 1midnight 2 1night 2 1now 1 1of 1 1past 2 1stormy 2 1the 1 2the 2 2their 1 1time 1 1time 2 1to 1 2was 2 2
Doc # Freq2 11 11 12 11 11 12 12 11 11 12 11 12 12 11 12 12 11 11 12 12 11 22 21 11 12 11 22 2
Term N docs Tot Freqa 1 1aid 1 1all 1 1and 1 1come 1 1country 2 2dark 1 1for 1 1good 1 1in 1 1is 1 1it 1 1manor 1 1men 1 1midnight 1 1night 1 1now 1 1of 1 1past 1 1stormy 1 1the 2 4their 1 1time 2 2to 1 2was 1 2
2002.10.31 - SLIDE 22IS 202 – FALL 2002
Inverted Indexes
• Permit fast search for individual terms• For each term, you get a list consisting of:
– Document ID – Frequency of term in doc (optional) – Position of term in doc (optional)
• These lists can be used to solve Boolean queries:
• country -> d1, d2• manor -> d2• country AND manor -> d2
• Also used for statistical ranking algorithms
2002.10.31 - SLIDE 23IS 202 – FALL 2002
How Inverted Files are Used
Dictionary PostingsDoc # Freq
2 11 11 12 11 11 12 12 11 11 12 11 12 12 11 12 12 11 11 12 12 11 22 21 11 12 11 22 2
Term N docs Tot Freqa 1 1aid 1 1all 1 1and 1 1come 1 1country 2 2dark 1 1for 1 1good 1 1in 1 1is 1 1it 1 1manor 1 1men 1 1midnight 1 1night 1 1now 1 1of 1 1past 1 1stormy 1 1the 2 4their 1 1time 2 2to 1 2was 1 2
Query on
“time” AND “dark”
2 docs with “time” in dictionary ->
IDs 1 and 2 from posting file
1 doc with “dark” in dictionary ->
ID 2 from posting file
Therefore, only doc 2 satisfied the query
2002.10.31 - SLIDE 24IS 202 – FALL 2002
Lecture Overview
• Review– Content Analysis– Statistical Properties of Text
• Zipf Distribution• Statistical Dependence
– Indexing and Inverted Files
• Vector Representation• Term Weights• Vector Matching• Clustering
Credit for some of the slides in this lecture goes to Marti Hearst
2002.10.31 - SLIDE 25IS 202 – FALL 2002
Document Vectors
• Documents are represented as “bags of words”
• Represented as vectors when used computationally– A vector is like an array of floating point– Has direction and magnitude– Each vector holds a place for every term in
the collection– Therefore, most vectors are sparse
2002.10.31 - SLIDE 26IS 202 – FALL 2002
Vector Space Model
• Documents are represented as vectors in term space– Terms are usually stems– Documents represented by binary or weighted vectors
of terms
• Queries represented the same as documents• Query and Document weights are based on
length and direction of their vector• A vector distance measure between the query
and documents is used to rank retrieved documents
2002.10.31 - SLIDE 27IS 202 – FALL 2002
Vector Representation
• Documents and Queries are represented as vectors
• Position 1 corresponds to term 1, position 2 to term 2, position t to term t
• The weight of the term is stored in each position
absent is terma if 0
,...,,
,...,,
21
21
w
wwwQ
wwwD
qtqq
dddi itii
2002.10.31 - SLIDE 28IS 202 – FALL 2002
Document Vectors
ID nova galaxy heat h'wood film role diet furA 10 5 3B 5 10C 10 8 7D 9 10 5E 10 10F 9 10G 5 7 9H 6 10 2 8I 7 5 1 3
“Nova” occurs 10 times in text A“Galaxy” occurs 5 times in text A“Heat” occurs 3 times in text A(Blank means 0 occurrences.)
2002.10.31 - SLIDE 29IS 202 – FALL 2002
Document Vectors
ID nova galaxy heat h'wood film role diet furA 10 5 3B 5 10C 10 8 7D 9 10 5E 10 10F 9 10G 5 7 9H 6 10 2 8I 7 5 1 3
“Hollywood” occurs 7 times in text I“Film” occurs 5 times in text I“Diet” occurs 1 time in text I“Fur” occurs 3 times in text I
2002.10.31 - SLIDE 30IS 202 – FALL 2002
Document Vectors
ID nova galaxy heat h'wood film role diet furA 10 5 3B 5 10C 10 8 7D 9 10 5E 10 10F 9 10G 5 7 9H 6 10 2 8I 7 5 1 3
2002.10.31 - SLIDE 31IS 202 – FALL 2002
We Can Plot the Vectors
Star
Diet
Doc about astronomyDoc about movie stars
Doc about mammal behavior
2002.10.31 - SLIDE 32IS 202 – FALL 2002
Documents in 3D Space
Primary assumption of the Vector Space Model: Documents that are “close together” in space are similar in meaning
2002.10.31 - SLIDE 33IS 202 – FALL 2002
Vector Space Documents and Queries
docs t1 t2 t3 RSV=Q.DiD1 1 0 1 4D2 1 0 0 1D3 0 1 1 5D4 1 0 0 1D5 1 1 1 6D6 1 1 0 3D7 0 1 0 2D8 0 1 0 2D9 0 0 1 3
D10 0 1 1 5D11 1 0 1 4Q 1 2 3
q1 q2 q3
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
t2
t3
t1
Boolean term combinationsQ is a query – also represented as a vector
2002.10.31 - SLIDE 34IS 202 – FALL 2002
Documents in Vector Space
t1
t2
t3
D1
D2
D10
D3
D9
D4
D7
D8
D5
D11
D6
2002.10.31 - SLIDE 35IS 202 – FALL 2002
Lecture Overview
• Review– Content Analysis– Statistical Properties of Text
• Zipf Distribution• Statistical Dependence
– Indexing and Inverted Files
• Vector Representation• Term Weights• Vector Matching• Clustering
Credit for some of the slides in this lecture goes to Marti Hearst
2002.10.31 - SLIDE 36IS 202 – FALL 2002
Assigning Weights to Terms
• Binary Weights
• Raw term frequency
• tf*idf– Recall the Zipf distribution– Want to weight terms highly if they are
• Frequent in relevant documents … BUT• Infrequent in the collection as a whole
• Automatically derived thesaurus terms
2002.10.31 - SLIDE 37IS 202 – FALL 2002
Binary Weights
• Only the presence (1) or absence (0) of a term is included in the vector
docs t1 t2 t3D1 1 0 1D2 1 0 0D3 0 1 1D4 1 0 0D5 1 1 1D6 1 1 0D7 0 1 0D8 0 1 0D9 0 0 1
D10 0 1 1D11 1 0 1
2002.10.31 - SLIDE 38IS 202 – FALL 2002
Raw Term Weights
• The frequency of occurrence for the term in each document is included in the vector
docs t1 t2 t3D1 2 0 3D2 1 0 0D3 0 4 7D4 3 0 0D5 1 6 3D6 3 5 0D7 0 8 0D8 0 10 0D9 0 0 1
D10 0 3 5D11 4 0 1
2002.10.31 - SLIDE 39IS 202 – FALL 2002
Assigning Weights
• tf*idf measure:– Term frequency (tf)– Inverse document frequency (idf)
• A way to deal with some of the problems of the Zipf distribution
• Goal: Assign a tf*idf weight to each term in each document
2002.10.31 - SLIDE 40IS 202 – FALL 2002
tf*idf
)/log(* kikik nNtfw
log
Tcontain that in documents ofnumber the
collection in the documents ofnumber total
in T termoffrequency document inverse
document in T termoffrequency
document in term
nNidf
Cn
CN
Cidf
Dtf
DkT
kk
kk
kk
ikik
ik
2002.10.31 - SLIDE 41IS 202 – FALL 2002
Inverse Document Frequency
• IDF provides high values for rare words and low values for common words
41
10000log
698.220
10000log
301.05000
10000log
010000
10000log
For a collectionof 10000 documents(N = 10000)
2002.10.31 - SLIDE 42IS 202 – FALL 2002
Similarity Measures
|)||,min(|
||
||||
||
||||
||||
||2
||
21
21
DQ
DQ
DQ
DQ
DQDQ
DQ
DQ
DQ
Simple matching (coordination level match)
Dice’s Coefficient
Jaccard’s Coefficient
Cosine Coefficient
Overlap Coefficient
2002.10.31 - SLIDE 43IS 202 – FALL 2002
tf*idf Normalization
• Normalize the term weights (so longer vectors are not unfairly given more weight)– Normalize usually means force all values to
fall within a certain range, usually between 0 and 1, inclusive
t
k kik
kikik
nNtf
nNtfw
1
22 )]/[log()(
)/log(
2002.10.31 - SLIDE 44IS 202 – FALL 2002
Vector Space Similarity
• Now, the similarity of two documents is:
• This is also called the cosine, or normalized inner product – The normalization was done when weighting
the terms
),( 1
t
kjkikji wwDDsim
2002.10.31 - SLIDE 45IS 202 – FALL 2002
Vector Space Similarity Measure
• Combine tf and idf into a similarity measure
)()(
),(
:combined
are comparison similarity theandion normalizat otherwise
),( :normalized are weights termif
absent is terma if 0 ...,,
,...,,
1
2
1
2
1
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,21
21
t
jd
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qtqq
dddi
ij
ij
ij
itii
ww
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DQsim
wwDQsim
wwwwQ
wwwD
2002.10.31 - SLIDE 46IS 202 – FALL 2002
Computing Similarity Scores
98.0cos
74.0cos
)8.0 ,4.0(
)7.0 ,2.0(
)3.0 ,8.0(
2
1
2
1
Q
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1.0
0.8
0.6
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0.60.4 1.00.2
0.2
2002.10.31 - SLIDE 47IS 202 – FALL 2002
What’s Cosine Anyway?
“One of the basic trigonometric functions encountered in trigonometry. Let theta be an angle measured counterclockwise from the x-axis along the arc of the unit circle. Then cos(theta) is the horizontal coordinate of the arcendpoint. As a result of this definition, the cosine function is periodic with period 2pi.”
From http://mathworld.wolfram.com/Cosine.html
2002.10.31 - SLIDE 48IS 202 – FALL 2002
Cosine vs. Degrees
Cosine
Degrees
2002.10.31 - SLIDE 49IS 202 – FALL 2002
Computing a Similarity Score
98.0 42.0
64.0
])7.0()2.0[(*])8.0()4.0[(
)7.0*8.0()2.0*4.0(),(
yield? comparison similarity their doesWhat
)7.0,2.0(document Also,
)8.0,4.0(or query vect have Say we
22222
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DQsim
D
Q
2002.10.31 - SLIDE 50IS 202 – FALL 2002
Vector Space Matching
1.0
0.8
0.6
0.4
0.2
0.80.60.40.20 1.0
D2
D1
Q
1
2
Term B
Term A
Di=(di1,wdi1;di2, wdi2;…;dit, wdit)Q =(qi1,wqi1;qi2, wqi2;…;qit, wqit)
t
j
t
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t
j dq
i
ijj
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wwDQsim
1 1
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)()(),(
Q = (0.4,0.8)D1=(0.8,0.3)D2=(0.2,0.7)
98.042.0
64.0
])7.0()2.0[(])8.0()4.0[(
)7.08.0()2.04.0()2,(
2222
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74.058.0
56.),( 1 DQsim
2002.10.31 - SLIDE 51IS 202 – FALL 2002
Weighting Schemes
• We have seen something of– Binary– Raw term weights– TF*IDF
• There are many other possibilities– IDF alone– Normalized term frequency
2002.10.31 - SLIDE 52IS 202 – FALL 2002
Term Weights in SMART
• SMART is an experimental IR system developed by Gerard Salton (and continued by Chris Buckley) at Cornell
• Designed for laboratory experiments in IR – Easy to mix and match different weighting
methods– Really terrible user interface– Intended for use by code hackers (and even
they have trouble using it)
2002.10.31 - SLIDE 53IS 202 – FALL 2002
Term Weights in SMART
• In SMART weights are decomposed into three factors:
norm
collectfreqw kkd
kd
2002.10.31 - SLIDE 54IS 202 – FALL 2002
SMART Freq Components
1)ln(
)max(2
1
2
1)max(
}1,0{
kd
kd
kd
kd
kd
kd
freq
freqfreq
freq
freq
freq
Binary
maxnorm
augmented
log
2002.10.31 - SLIDE 55IS 202 – FALL 2002
Collection Weighting in SMART
k
k
k
k
k
k
Doc
Doc
DocNDocDoc
NDoc
Doc
NDoc
collect
1
log
log
log
2
Inverse
squared
probabilistic
frequency
2002.10.31 - SLIDE 56IS 202 – FALL 2002
Term Normalization in SMART
jvector
vectorj
vectorj
vectorj
w
w
w
w
norm
max
4
2
sum
cosine
fourth
max
2002.10.31 - SLIDE 57IS 202 – FALL 2002
To Think About
• How does the tf*idf ranking algorithm behave?– Make a set of hypothetical documents
consisting of terms and their weights– Create some hypothetical queries– How are the documents ranked, depending on
the weights of their terms and the queries’ terms?
2002.10.31 - SLIDE 58IS 202 – FALL 2002
Document Space Has High Dimensionality
• What happens beyond 2 or 3 dimensions?
• Similarity still has to do with how many tokens are shared in common
• More terms -> harder to understand which subsets of words are shared among similar documents
• One approach to handling high dimensionality: Clustering
2002.10.31 - SLIDE 59IS 202 – FALL 2002
Vector Space Visualization
2002.10.31 - SLIDE 60IS 202 – FALL 2002
Text Clustering
• Finds overall similarities among groups of documents
• Finds overall similarities among groups of tokens
• Picks out some themes, ignores others
2002.10.31 - SLIDE 61IS 202 – FALL 2002
Text Clustering
Clustering is“The art of finding groups in data.” -- Kaufmann and Rousseeu
Term 1
Term 2
2002.10.31 - SLIDE 62IS 202 – FALL 2002
Text Clustering
Clustering is“The art of finding groups in data.” -- Kaufmann and Rousseeu
Term 1
Term 2
2002.10.31 - SLIDE 63IS 202 – FALL 2002
Pair-Wise Document Similarity
ID nova galaxy heat h'wood film role diet furA 1 3 1B 5 2C 2 1 5D 4 1
How to compute document similarity?
2002.10.31 - SLIDE 64IS 202 – FALL 2002
Pair-Wise Document Similarity
t
iii
t
t
wwDDsim
wwwD
wwwD
12121
2,22212
1,12111
),(
...,,
...,,
9)11()42(),(
0),(
0),(
0),(
0),(
11)32()51(),(
DCsim
DBsim
CBsim
DAsim
CAsim
BAsim
ID nova galaxy heat h'wood film role diet furA 1 3 1B 5 2C 2 1 5D 4 1
(no normalization for simplicity)
2002.10.31 - SLIDE 65IS 202 – FALL 2002
Document/Document Matrix
....
.....
.....
....
....
...
21
2212
1121
21
nnn
t
t
t
ddD
ddD
ddD
DDD
jiij DDd to of similarity
2002.10.31 - SLIDE 66IS 202 – FALL 2002
Agglomerative Clustering
A B C D E F G HI
2002.10.31 - SLIDE 67IS 202 – FALL 2002
Agglomerative Clustering
A B C D E F G HI
2002.10.31 - SLIDE 68IS 202 – FALL 2002
Agglomerative Clustering
A B C D E F G HI
2002.10.31 - SLIDE 69IS 202 – FALL 2002
ClusteringAgglomerative methods: Polythetic, Exclusive or Overlapping, Unorderedclusters are order-dependent
1. Select initial centers (i.e., seed the space)2. Assign docs to highest matching centers and compute centroids3. Reassign all documents to centroid(s)
DocDoc
DocDoc
DocDoc
DocDoc
Rocchio’s method
2002.10.31 - SLIDE 70IS 202 – FALL 2002
Automatic Class Assignment
Doc
Doc
Doc
Doc
Doc
Doc
Doc
SearchEngine
1. Create pseudo-documents representing intellectually derived classes.2. Search using document contents3. Obtain ranked list4. Assign document to N categories ranked over threshold. OR assign to top-ranked category
Automatic Class Assignment: Polythetic, Exclusive or Overlapping, usually orderedclusters are order-independent, usually based on an intellectually derived scheme
2002.10.31 - SLIDE 71IS 202 – FALL 2002
K-Means Clustering
1) Create a pair-wise similarity measure
2) Find K centers using agglomerative clustering
– Take a small sample – Group bottom up until K groups found
3) Assign each document to nearest center, forming new clusters
4) Repeat 3 as necessary
2002.10.31 - SLIDE 72IS 202 – FALL 2002
Scatter/Gather
• Cutting, Pedersen, Tukey & Karger 92, 93, Hearst & Pedersen 95
• Cluster sets of documents into general “themes”, like a table of contents
• Display the contents of the clusters by showing topical terms and typical titles
• User chooses subsets of the clusters and re-clusters the documents within
• Resulting new groups have different “themes”
2002.10.31 - SLIDE 73IS 202 – FALL 2002
S/G Example: Query on “star”
Encyclopedia text14 sports
8 symbols 47 film, tv 68 film, tv (p) 7 music97 astrophysics 67 astronomy(p) 12 stellar phenomena10 flora/fauna 49 galaxies, stars
29 constellations 7 miscelleneous
Clustering and re-clustering is entirely automated
2002.10.31 - SLIDE 77IS 202 – FALL 2002
Clustering Result Sets
• Advantages:– See some main themes
• Disadvantage:– Many ways documents could group together
are hidden
• Thinking point: What is the relationship to classification systems and facets?
2002.10.31 - SLIDE 78IS 202 – FALL 2002
Next Time
• Probabilistic Models
• Relevance Feedback