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GUHA - a summary
1. GUHA (General Unary Hypotheses Automaton) is a method of automatic
generation of hypotheses based on empirical data, thus a method of data mining.
GUHA is one of the oldest methods of data mining - GUHA was introduced in
Hájek P., Havel I., Chytil M.: The GUHA method of automatic hypotheses
determination, Computing 1 (1966) 293-308.
- and GUHA still develops. GUHA is a kind of automated exploratory data analysis: it
generates systematically hypotheses supported by the data.
2. GUHA is primary suitable for exploratory analysis of large data.
The processed data form a rectangle matrix, where rows corresponds to objects
belonging to the sample and each column corresponds to one investigated variable. A
typical data matrix processed by GUHA has hundreds or thousands of rows and tens of
columns.
Exploratory analysis means that there is no single specific hypothesis that should be
tested by our data; rather, our aim is to get orientation in the domain of investigation,
analyse the behaviour of chosen variables, interactions among them etc. Such inquiry is
not blind but directed by some general (possibly vague) direction of research (some
general problem).
GUHA - a summary
3. GUHA systematically creates all hypotheses interesting from the point of view of
a given general problem and on the base of given data.
This is the main principle: “all interesting hypotheses”. Clearly, this contains a
dilemma: "all'' means most possible, "only interesting'' means "not too many''. To cope
with this dilemma, one may use different GUHA procedures and, having selected one,
by fixing in various ways its numerous parameters. (The program leads the user and
makes the selection of parameters easy.)
Three remarks:
* GUHA procedures polyfactorial hypotheses i.e. not only hypotheses relating one
variable with another one, but expressing relations among single variables, pairs, triples,
quadruples of variables etc.
* GUHA offers hypotheses. Exploratory character implies that the hypotheses produced
by the computer (numerous in number: typically tens or hundreds of hypotheses) are
just supported by the data, not verified. You are assumed to use this offer as inspiration,
and possibly select some few hypotheses for further testing.
*GUHA is not suitable for testing a single hypothesis: routine packages are good for
this.
GUHA - a summary
4. The GUHA procedure generates statements on association between complex
Boolean attributes. These attributes are constructed from the predicates
corresponding to the columns of the data matrix.
Each such predicate (attribute) endowed with a (finite) set of categories, each category
being by a subset of the range of the predicate. A literal has the form PRED(CAT)
where PRED is a predicate and CAT one of its categories (e.g. TEMPERATURE:(>38)
etc.)
A hypothesis (or better: an observational statement) has a form
 ( is associated with )
where the attributes ,  are built from literals (unary predicates) using Boolean
connectives , , , (conjunction, disjunction, negation). Typically only some Boolean
attributes are allowed, e.g. only conjunctions of finitely many literals, containing each
predicate at most once, for example
TEMPERATURE: (>38 °C)  PRESURE: (HIGH)  SEX (MALE)
GUHA - a summary
5. Given the data (a model M), each pair of Boolean attributes ,  determines its
four-fold frequency table; the association of  with  is defined by choosing an
associational quantifier  i.e. a function assigning to each four-fold table either 1
(associated) or 0 (not associated) and satisfying some natural monotonicity
conditions. The formula  is true in the data iff the function defining  gives 1
(TRUE) to the four-fold table given by , 
The four-fold table has the form:
 ¬
 a
b
r
 c d
s
k
l
m
where a is the number of objects in the data satisfying both  and ;
b is the number of objects in the data satisfying  but not ;
c is the number of objects in the data not satisfying  but satisfying ;
d is the number of objects in the data not satisfying  nor ;
r=a+b, s=c+d, k=a+c, l=b+d and m=a+b+c+d.
Association means, roughly, that there are enough coincidences (a, d are big enough)
and not too many differences (b, c are not too big). Thus, a quantifier  is associational
if vM()= TRUE and a'a, b'b, c'c, d' d imply vN()= TRUE , too.
GUHA - a summary
6. There are various types of associational quantifiers, formalising various kind of
associations; among them implicational quantifiers formalise the association "many
 are ''. Comparative quantifiers formalise the association " makes  more
likely than .'' Some quantifiers just express observations on the data, some
others serve as tests of statistical hypotheses on unknown probabilities.
Not all quantifiers are associational; the implicational ones do not depend on c, d the
comparative ones are symmetric:  implies  and admit negation:  implies
 . Various quantifiers are used.
7. The input for the GUHA consists of
(1) the data matrix and (2) parameters determining symbolic restriction to the
pairs , of Boolean attributes (antecedent - succedent) to be generated, the
quantifier to be used and a few other things.
In particular, one has to declare predicates that can occur in the antecedent and the
succedent, minimal and maximal length of antecedent/succedent (number of literals
occurring), the kind and parameters of the quantifier used, kind of processing of
missing data (if any; three possibilities) etc.
GUHA - a summary
8. The core program LISp Minor produces all associations  satisfying the
syntactic restrictions and true in the data.
The generation is not done blindly but uses various techniques serving to avoid
exhaustive search. The found associations together with various parameters are not
mechanically printed but saved in a solution file for further processing.
9. The program for interpretation of results enables the user to browse the
associations format, sort them according to various criteria, select reasonably
defined subsets and output concise information of various kinds.
10. The GUHA method has deep logical and statistical foundations, continuously
developed further
GUHA is being further developed at the institute of Computer Science of the Academy
of Sciences of the Czech Republic (Petr Hajek and his group) and at the Prague
University of Economics under the name LISp-Miner (Jan Rauch and his group).
Example. Assume we are observing children who have an allergic reaction to, say, tomato, apple,
orange, cheese or milk. Thus, we have observations as ‘Child x is allergic to milk’, ‘Child y is
allergic to cheese’, ‘Child z is allergic to tomato’, etc. We write shorter ‘Milk(x)’, ‘Cheese(y)’
and ‘Tomato(z)’, etc.
Child
Tomato
Apple
Orange
Cheese
Milk
Milk(-), Cheese(-), Tomato(-), Orange(-) and Anna
1
1
0
1
1
Apple(-) are (unary) predicates of our obser- Aina
1
1
1
0
0
1
1
1
1
1
vational language and x, y, z,… are variables. Naima
Rauha
0
1
1
0
1
Expressions ‘Milk(x)’, ‘Chese(y)’ etc. are
Kai
0
1
0
1
1
atomic (open) formulae. Combine formulae
Kille
1
1
0
0
1
by logical connectives (not),  (and)  (or), Lempi
0
1
1
1
1
e.g. Milk(x)   Cheese(y) would mean
Ville
1
0
0
0
0
‘Child x is allergic to milk and
Ulle
1
1
0
1
1
Dulle
1
0
1
0
0
Child y is not allergic to cheese’
Dof
1
0
1
0
1
However, in stead of open formulae, we are
Kinge
0
1
1
0
1
more interested in universal closed formulae,
Laade
0
1
0
1
1
e.g. ‘All children are allergic to milk‘, ‘Most Koff
1
1
0
0
1
children are not allergic to orange’, ‘There is Olavi
0
1
1
1
1
a child allergic to tomato’, ‘In most cases, if Our observations support the the following
a child is allergic to milk then she/he is allergic statements:
to cheese’; write x Milk(x), Wx¬Orange(x), *There is a child allergic to tomato
xTomato(x),
x(Milk(x),Cheese(x)). * In all cases, if a child is allergic to cheese then
The following table (matrix) with 5 columns
she/he is allergic to milk, too
and 15 rows results our observations, where
* Not all children are allergic to tomato
‘0’ and ‘1’ have the obvious meaning.
* Most children are allergic to milk
We define what is
• data (in this context)
• data mining, its goals and outcomes
• GUHA in general
1h
In GUHA-theory we define what is
• an observational predicate language
• a model
• TRUE/FALSE in a model
• a four-fold contingency table
 basic equivalenc e
ad
p
abcd
The following is probability theoretically
justified associational quantifier
 Above average quantifier
a
(1  p)(a  c)

a b abcd
We study non-standard quatifiers
• associational better quantifiers
• implicational better quantifiers
• IB quantifier are AB quantifiers
• interesting quantifiers
The following statistically justified
associational quantifiers
• Fisher quantifier
• c2 quantifier
The following are logically justified
associational quantifiers
We study how e.g. these quantifiers
are implemented to a system
LISp Miner
a
p
ab
a
 basic double implicatio n
p
abc
 basic implicatio n
4 -5 h
3-4h