Download Relational data mining in finance

Relational Data Mining in
Finance
Haonan Zhang
CFWin03-37
[email protected]
03/04/2003
Organization
Motivation & Introduction
 Background
 Problem statement
 Solution
 Outcome
 Conclusion and future work

Motivation & Introduction

Motivation: Analyze finance data, find and extract hidden patterns
and relations between data. Thus the result will make good
support for decision making in finance.

To predict financial markets is a complex and challenging task
because several reasons:



The dimensionality of the problem is high.
The relationships among independent and dependent variable are
weak and non-linear.
Data mining is a process that analyze data from different
perspectives and explicitly show the interaction between data with
a given confidence. It is suitable for predict financial markets
Motivation & Introduction cont..

Traditional data mining methods have their limitations,
such as lack of knowledge representation and limited
forms of background knowledge.

The Inductive Logic Programming (ILP) and Relational
Data mining (RDM) can overcome these limitations.

The ILP can naturally incorporate background knowledge and
relations between objects into the leaning process;

The RDM can discover hidden relations (general first order
relations) in numerical and symbolic data using background
knowledge (domain theory)
Background

A predicate is a binary function. A predicate can be
defined extensionally as a set of tuples for which the
predicate is true, or intentionally as a set of (Horn)
clauses for computing whether the predicate is true.

A literal is a predicate or its negation

A horn clause consists of two parts: a clause head and
a clause body. A clause head is defined as a single
predicate. A clause body is defined as a conjunction of
literals.
Background cont..

FOIL (First Order Inductive Learning) algorithm
 Input: a target relation to be learned, a set of positive, a set of
negative examples of the relation, and a set of background
relations.
 Learning approach: separate-and-conquer approach: It learns
a clause at a time, then it remove training examples covered
by the clause, then begin to learn the subsequent clauses.
FOIL tries to cover as much as positive training examples and
cover no negative training examples. The algorithm will
terminate when all positive training examples are covered.
 Output: a set of clauses that describe the target relation.
Background cont..

The FOCL (First Order Combined Learner)
algorithm extends the FOIL algorithm in
several ways.
 The FOCL constrains the search by using variable
typing, and inter-argument constraints.
 The FOCL uses background knowledge to improve
the learning process, such as rules which are
defined by a collection of examples, and a partial
possible incorrect rule which is an initial
approximation of the predicate to be learned.
Background cont..

The MMDR (Machine Method for Discovering
Regularities) algorithm focuses on generating
probabilistic first-order rules and measurement
issues for numerical relational

MMDR permits various forms of background
knowledge, such as constraints, predefined
predicates and partial (may be incorrect) rules.

MMDR uses the statistical significance of
hypotheses and the strength of data types scales,
to limit the search space.
Problem statement

The FOIL input a
target relation to be
learned, a set of
positive and a set of
negative examples,
and a set of
background
knowledge.

The output would
be a set of clauses
that describe the
target relation using
background
knowledge
Solution

separate-and-conquer approach
Solution cont..

Two kinds of literals can be appended to develop a
clause.

gainful literals: literals may increase the covering of
positive examples. Gainful literals are evaluated using
information heuristic. The average information provided by
discovery that literal of the bindings is
When new literal m is added, suppose that some of
the bindings are excluded, and k of then+ bindings
are not excluded. The total gain is
Solution cont..

determinate literals: A determinate literal introduces new
variable. The new partial clause has the same binding for
each positive binding of current clause, and at most one
binding for each negative binding of current clause. Therefore,
sometimes determinate literal has zero gain.
Solution cont..

Four forms of literals are considered can appear in a
clause:
Solution cont..

In order to learn recursive theories without leading to
infinite recursion, FOIL uses three approaches to
assure that the recursive literals are risk free.

Ordering constants: the algorithm can discover an ordering of
constant and order constants.

Ordering pairs of variables: when a type’s constant is ordered,
the ordering of a pair of variable Vi and Vj of same type in a
partial clause may also exist.

Ordering recursive literals: the ordering among variables can
be extended to an ordering of literals involving a predicate and
variables.
Flow Diagram
Outcome
Conclusion and future work

The FOIL algorithm can effectively find the hidden
relations between target relation and background
knowledge and represent the target relation using
background knowledge.

The FOIL algorithm uses a very complex recursive
control and backup scheme, which increase the
complexity of the algorithm. Further implementation
needs a better understanding of these schemes.

Future work: implement the FOIL algorithm in parallel
computers.