USER-DRIVEN

ASSOCIATION RULE MINING USING A LOCAL

ALGORITHM

Claudia Marinica

, Andrei Olaru

1,2

and Fabrice Guillet

LINA Laboratory, Ecole Polytechnique de l’Universite de Nantes

rue Christian Pauc BP 50609 44306 Nantes Cedex 3, France

Departement of Computer Science, University Politehnica of Bucharest

Splaiul Independentei 313 060042 Bucharest, Romania

Keywords:

Association Rules, Mining Algorithms, User-Driven Mining, Rule Interestingness, Subjective Measures.

Abstract:

One of the main issues in the process of Knowledge Discovery in Databases is the Mining of Association

Rules. Although a great variety of pattern mining algorithms have been designed to this purpose, their main

problems rely on in the large number of extracted rules, that need to be ﬁltered in a post-processing step

resulting in fewer but more interesting results. In this paper we suggest a new algorithm, that allows the user

to explore the rules space locally and incrementally. The user interests and preferences are represented by

means of the new proposed formalism - the Rule Schemas. The method has been successfully tested on the

database provided by Nantes Habitat.

1 INTRODUCTION

Knowledge Discovery in Databases is the non-trivial

process of identifying valid, novel, potentially use-

ful, and ultimately understandable patterns in data

(Fayyad et al., 1996). Association Rule Mining

(Agrawal et al., 1993) is an important technique of

data mining, an Association Rule being deﬁned as an

implication of the form a → b, that shows that the

presence of itemset a in a transaction implies, with a

certain conﬁdence c, the presence of itemset b in the

same transaction.

Association rules may obtain valuable informa-

tion from large databases, but the number of rules

extracted by classic algorithms is so large that it is

impossible for un user to ﬁnd himself the interest-

ing ones, knowing that most rules are not interest-

ing, already known or with no consequence in action

(Piatetsky-Shapiro and Matheus, 1994), (Silberschatz

and Tuzhilin, 1995). Generally, the interestingness

of rules depends on objective (statistical) measures

as the support and the conﬁdence. Complementary,

subjective measures were proposed in order to ex-

tract those interesting rules as compared to user back-

ground knowledge and user expectations. The two

main subjective measures of interest are unexpected-

ness – rules surprising to the user – and actionability

– rules helping the user to take actions (Silberschatz

and Tuzhilin, 1995).

Although, usually, the reduction of the number of

rules is done in the post-processing phase. Thus, the

entire set of association rules is mined, most of them

being, after all, considered as not interesting and elim-

inated. As a consequence, the whole process become

quite inefﬁcient (Silberschatz and Tuzhilin, 1995).

This paper proposes to introduce post-mining

principles into the mining step, focusing on interest-

ing rules without the necessity of extracting all rules

existing in the database. The user may explore the

rule space incrementally, a small amount at each step

(Blanchard et al., 2007), starting from his/her own be-

liefs and knowledge and discovering rules related to

these goals: conﬁrming rules, specialized rules, gen-

eralized rules or exception rules. At each step the

user chooses the most relevant rules for further ex-

ploration. This approach is based on a novel, ﬂexible

and unitary speciﬁcation language that we propose in

order to represent user interests – the Rule Schema

with the 4 Operations that can be applied on – and on

a novel local mining algorithm that we designed, gen-

erating a local set of candidate rules – as all possible

rules that may result from applying the Operations to

the Rule Schemas. This way, global post-processing

is avoided in favor of a local and focused rule explo-

200

Marinica C., Olaru A. and Guillet F. (2009).

USER-DRIVEN ASSOCIATION RULE MINING USING A LOCAL ALGORITHM.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

200-205

DOI: 10.5220/0002003002000205

 SciTePress

ration that however is globally valid.

The proposed method has been tested on a real-life

database, provided courtesy of the Nantes Habitat

agency. Several interesting experiments have been

carried out and relevant results have been obtained.

The paper is organized as follows. Section 2

presents the research domain and reviews related

works. Section 3 describes the Rule Schema formal-

ism with the proposed Operations that the user can

perform on, and presents the local mining algorithm.

Results are discussed in section 4. Finally, section 5

presents the conclusion.

2 RELATED WORK

2.1 Association Rule Mining

The association rule mining technique (Agrawal et al.,

1993) is applied over databases described as D =

{I, T }. Let I = {I = 1, I

, ...,I

} be a set of attributes

(called items) and let T = {t

, . . .t

} be the transac-

tion set. Each transaction t

= {I

, I

, . . . , I

} is a set

of items, such as t

⊂ I and each set of items, X, is

called itemset.

An association rule is an implication X → Y ,

where X and Y are two itemsets and X ∩ Y =

This rule holds on T with the conﬁdence c if c%

of transactions in T that contain X, also contain Y.

The rule has support s in transaction set T if s% of

transactions contain X ∪Y.

2.2 Mining Algorithms

Over the last decade, a great variety of rule min-

ing algorithms was proposed starting with classic al-

gorithms, passing by condensed representation algo-

rithms and, ending with, incomplete set algorithms

and inference generation techniques (Ceglar and Rod-

dick, 2006). Classic algorithms, classiﬁed in candi-

date generation and pattern growth algorithm, extract

all valid itemsets from data. Apriori (Agrawal et al.,

1996) is the basic pattern generation algorithm and an

important base for many algorithms proposed since.

This type of algorithms is described on two steps:

identifying candidate itemsets and validating the can-

didates. However, it is difﬁcult to modify the pruning

strategy in order not to extract all patterns, but only

the ones that might be interesting to the user. In fact,

We would like to thank Nantes Habitat, the Public Housing

Unit in Nantes, France, and especially to Ms. Cristelle Le Bouter

for supporting this work.

most of the usual pattern-mining algorithms extract

all patterns that have a minimum support. Moreover,

most algorithms extract patterns, and not rules, asso-

ciation rules being built by using the mined itemsets.

Pattern growth algorithms, extended by several

approaches, were introduced for the ﬁrst time with

the fundamental algorithm FP-Growth (Pei and Han,

2000). This algorithm simpliﬁes the process of item-

set generation creating complex hyperstructures: one

structure describing the items with their frequency

and the other being represented by the frequent pat-

tern tree. Nevertheless, this technique is not conve-

nient for big databases, due to memory problems.

Condensed representation algorithms and incom-

plete set of algorithms generate a subset of valid item-

sets from which all itemsets can be derived, but this

idea is not related to our work.

Closer to our paper ideas, Padmanabhan and

Tuzilin proposed the inference generation reduction

incorporating user knowledge in the mining algorithm

(Padmanabhan and Tuzhilin, 1998). Thus, starting

from a user belief and applying an Apriori-like local

algorithm, a set of rules satisfying several constraints

is generated.

2.3 Subjective Measures

Subjective measures and the integration of user be-

liefs were ﬁrst discussed in the paper of Silberschatz

and Tuzhilin (Silberschatz and Tuzhilin, 1995), in

which the most important two subjective interesting-

ness measures are introduced: unexpectedness and

actionability. Three cases of unexpectedness are gen-

erally considered, comparing discovered rules and

previous knowledge: unexpected condition, unex-

pected conclusion, and both condition and conclusion

unexpected (Liu et al., 1999).

The concept of rule templates is introduced by

Klemettinen (Klemettinen et al., 1994), as items in

two lists: inclusion – rules that are interesting, and

restriction – rules that are not interesting. However,

all rules must be extracted and the search is not done

locally. Moreover, the formalism is very simple and

not too ﬂexible. A logical representation and com-

parison for user beliefs has been suggested (Padman-

abhan and Tuzhilin, 1998), but this approach is fairly

limited. In (Li, 2006) the author proposed a frame-

work for the discovery of a family of optimal rule sets

for a range of interestingness metrics.

Liu presented (Liu et al., 1997) an interesting

representation of user beliefs. It contains three lev-

els of speciﬁcation: General Impressions (GI), Rea-

sonably Precise Concepts (RPC) and Precise Knowl-

edge (PK). All three formalisms use items in a tax-

USER-DRIVEN ASSOCIATION RULE MINING USING A LOCAL ALGORITHM

201

onomy, therefore allowing only for is-a relations be-

tween items. Moreover, using three different levels

of speciﬁcation might be difﬁcult to use, if the user

wants to combine their features.

A very early paper proposed the integration of

special structures for the representation of domain

knowledge and constraints (Anand et al., 1995):

Hierarchical Generalization Trees (HG-Trees), At-

tribute Relationship Rules (AR-rules) and Environ-

ment Based Constraints (EBC). Using item tax-

onomies was suggested in (Srikant and Agrawal,

1995), but, however, taxonomies are limited to is-a

relations. There are many advantages in using ontolo-

gies instead (Phillips and Buchanan, 2001).

Nevertheless, the number of rules extracted rests

by these methods too large making impossible the

post-analysis task, and an interactive process could

help the user to ﬁnd small sets of information.

3 USER-DRIVEN EXPLORATION

OF THE RULE SPACE

3.1 Rule Schema formalism

Interesting rules are in a certain relation – conﬁrma-

tion or contradiction – with the current beliefs of the

user. We propose a new formalism – Rule Schemas –

in order to express user beliefs and expectations about

the associations in the database.

Deﬁnition 1. A Rule Schema is represented as fol-

lows:

rs(Condition → Conclusion [General]) [s% c%]

where Condition and the Conclusion contain the

items that the user believes to be present in the an-

tecedent and, respectively, in the consequent of the

rule. The General part contains the items that the user

is not sure in which of the two other parts to place.

The three parts – Condition, Conclusion and

General – are all Expressions of the form Expr =

{Expr} | [Expr] | Expr? | Item – a disjunction or a

conjunction of Expressions or an optional Expression

(the items contained might not be present in the rule).

The Rule Schema also contains optional constraints

of support and conﬁdence.

This formalism is based on the speciﬁcation lan-

guage proposed in (Liu et al., 1999), but it improves it

by completely covering the three levels of speciﬁca-

tion presented in the General Impressions formalism

(Liu et al., 1999). If the Condition and Conclusion are

used, the Schema is more like a Reasonably Precise

Concept or Precise Knowledge. If the General part is

used, the Schema is more like a General Impression.

The improvement is that a Rule Schema may use all

three parts simultaneously.

3.2 Operations on Rule Schemas

Extending previous works (Blanchard et al., 2007),

we propose 4 Operations that allow the user to ex-

plore the rule space starting from his/her beliefs and

knowledge.

Conﬁrmation is the simplest operation that we pro-

pose. It ﬁlters all rules that contain the items in

Condition and in Conclusion in the antecedent, and

respectively, in the consequent, and the items in the

General part in any of the two sides of the implica-

tion. The items in the General part may be split in any

possible ways between the antecedent and the conse-

quent.

More formally, the searched rules are of the form:

Condition ∪ Subset → Conclusion ∪ (General −

Subset), for all Subset ⊆ General

Example. The Rule Schema rs([A] → [B] [C, D]) is

conﬁrmed by any of the four following rules:

A,C, D → B

A,C → B, D

A, D → B,C

A → B,C, D

k-Specialization, based on (Bayardo et al., 1999), al-

lows the user to ﬁnd the rules that have a more par-

ticular condition and the same conclusion, and which

improve the conﬁdence of the initial rule. That is, a

specialization of a → b [s1 c1] is a, c → b [s2 c2], if

c2 > c1.

k-Specialization is not performed directly on the

database, but on the rules resulting from the Con-

ﬁrmation of the initial Rule Schema. For the rules

of the form Condition → Conclusion obtained af-

ter the Conﬁrmation operation, results of the k-

Specialization are of the form:

Condition ∪ Set → Conclusion, for all |Set| = k

and Set ⊆ (I − (Condition ∪Conclusion)), with I the

full itemset.

Example. For a Rule Schema rs([A] → [B]) and

I = {A, B,C, D} the output of the 1-Specialization op-

eration may be:

A → B [s1 c1]

A,C → B [s2 c2]

A, D → B [s3 c3]

In the example above, it is required that the con-

ﬁdence measure satisﬁes c2 ≥ c1 and c3 ≥ c1. Obvi-

ously, for support, s2 ≤ s1 and s3 ≤ s1.

k-Generalization is the opposite of k-Specialization.

This operation ﬁnds the rules that have a more general

ICEIS 2009 - International Conference on Enterprise Information Systems

202

condition implying the same conclusion. Support is

expected to be higher and conﬁdence slightly lower.

Searched rules by k-Generalization operation are of

the form:

Condition − Set → Conclusion, for all |Set| = k

and Set ⊆ Condition.

k-Exception is an important operation, as it ﬁnds

rules with an unexpected conclusion, in the context

of a more specialized condition. That is, for rules

of the form a → B [s1 c1] exceptions are of the form

a, c → ¬B [s2 c2]. In (Duval et al., 2007) an exception

is considered valuable knowledge if, knowing that the

conﬁdence of c → ¬B is c3, then c2 ≥ c1, and c3 must

be fairly low, as it must not be c alone, but the associ-

ation with a that leads to ¬B. k-Exception operation

is the k-Specialization of the rules with negated con-

clusion.

3.3 Local Mining Algorithm

In our approach, the search for interesting rules be-

comes local: rules are searched in the neighbourhood

of rules and associations that the user already knows,

or that the user believes to be true, speciﬁed by means

of the Rule Schemas.

Example. Suppose the user wants to ﬁnd all 1-

Specialization rules of the Rule Schema rs([A] →

[{B,C}][D]) [10%, 60%]. That is, A leads to either

B or C, and they are associated with D. Support and

conﬁdence must be over 10% and 60% respectively.

The full item set is I = {A, B,C, D, E, F}. The algo-

rithm works as follows:

• ﬁrst, Expressions are expanded and the General

part is split between condition and conclusion.

Generated rules are then checked against the

database and candidates with support lower than

10% are pruned:

[A] → [B D] [25% 67%]

[A D] → [B] [25% 44%]

[A] → [C D] [8% 26%] – pruned

[A D] → [C] [8% 35%] – pruned

• based on the previous result, 1-Specialization op-

eration is performed (new items from I are added

to the condition). Candidates are checked and

pruned if support or conﬁdence are lower than the

threshold speciﬁed in the Schema or if there is no

improvement in conﬁdence.

[A C] → [B D] [7% 20%] – pruned for support

[A E] → [B D] [17% 62%] – pruned: ancestor’s

conﬁdence is not improved

[A F] → [B D] [20% 83%]

[A D C] → [B] [7% 35%] – pruned for support

[A D E] → [B] [9% 48%] – pruned for support

[A D F] → [B] [14% 66%]

• results are sorted according to conﬁdence:

[A F] → [B D] [20% 83%]

[A D F] → [B] [14% 66%]

There are a number of advantages that this ap-

proach has, compared to the Apriori algorithm. They

result in great part from the fact that the Rule Schemas

are partially instantiated, so the search space is greatly

reduced. Moreover, the more the user reﬁnes current

Rule Schemas, the lower is the number of generated

candidate rules.

Compared to Apriori, the number of passes

through the databases is lower. Once all candi-

date rules are generated, only one pass through the

database is necessary, to check the support of the can-

didates. For complexity reasons, in the case of multi-

level operations one pass per speciﬁcity / generality

level is necessary.

One important issue in the presented approach is

the number of generated candidate rules. This de-

pends on the operation and on the properties of par-

ticular Rule Schemas, as shown below.

For the operation of Conﬁrmation on a Rule

Schema rs(X → Y [Z]) (where X ,Y, Z are item sets),

the number of generated rules is equal to the number

of possibilities of splitting the Z set into two subsets:

|Z|

: for each subset S in Z, S is added to the condition

and Z − S to the conclusion. Usually, the number of

items in Z will be fairly low.

In Specialization, all the rules of the form X ∪S →

Y are generated, where S 6⊂ X ∪ Y and |S| the speci-

ﬁcity level. Normally, the number of candidate rules

would be C

|S|

|I|−|X∪Y|

where I contains all items in the

database. However, a more efﬁcient implementation

explores specialized rules one level at a time. Most

level 1 candidates are pruned, so the second level will

be based on much fewer rules, reducing the total num-

ber of generated candidate rules. The same approach

may be used for Exception.

4 RESULTS AND DISCUSSION

We have developed an application that implements the

algorithm described above and allows the manage-

ment of Rule Schemas. The application was tested

on a real-life database, provided by Nantes Habi-

tat, a public ofﬁce managing social accommodations

in Nantes, France. Each year, 1500 Nantes Habitat

custemers (out of a total of 50000) answer a question-

naire about the quality of their accommodation. In the

database, there exists one attribute for each question

USER-DRIVEN ASSOCIATION RULE MINING USING A LOCAL ALGORITHM

203

Question number and text

Q8 Neighborhood is quiet

Q18 Cleanness of the ﬂoor

Q26 State of the entry hall

Q29 State of the ﬂoor

Q60 Building corresponds to expectations

Q65 Technical interventions

Q92 NH services are adequate to needs

Q97 Price

S boi Boissiere neighbourhood

Figure 1: Meaning of questions in the example.

– with values of 1 to 4 for the decreasing satisfaction

level and one transaction for each questionnaire. A

set of questions with their meanings is presented in

Figure 1. To obtain binary attributes, items of the

form question=answer are formed, for example the

item Q35=1 represents an answer of ”very satisﬁed”

to the question number 35 – about the lighting of com-

mon spaces. Therefore there are a total of 624 items.

With classical algorithms and imposing thresholds of

8% for support and 85% for conﬁdence we extracted a

total of 1.528.978 rules, very difﬁcult to post-process,

even using a tool.

2-Specialization of

rs([Q97 = 4] → [Q26 = 4] [2% 95%])

The analyst is interested on what relates to the im-

plication of the dissatisfaction about price (Question

97) on the dissatisfaction about the state of the entry

hall (Question 26) – as a part of the common spaces

in the building. The search starts with a Rule Schema

containing unsatisﬁed answers (value 4) to two ques-

tions related to the problem: Q97 and Q26. Support

must be reasonable (2%) and conﬁdence must be high

(95%).

An operation of 2-Specialization has the follow-

ing output ﬁnding items that are related to the impli-

cation:

17 results.

[Q29=4, Q65=4, Q97=4] -> [Q26=4] [ 2.6% 97.5% ]

[Q29=4, Q32=4, Q97=4] -> [Q26=4] [ 2.4% 97.3% ]

[Q16=4, Q29=4, Q97=4] -> [Q26=4] [ 4.6% 97.1% ]

[Q29=4, Q60=1, Q97=4] -> [Q26=4] [ 2.2% 97.0% ]

[Q29=4, Q37=4, Q97=4] -> [Q26=4] [ 2.0% 96.7% ]

[Q18=4, Q31=4, Q97=4] -> [Q26=4] [ 2.0% 96.7% ]

...

The output shows a number of interesting relations:

apart from dissatisfaction about the state and clean-

ness of common spaces (entry hall, building level,

etc) and equipment (interphone), there is also an in-

dication about the efﬁciency with which the technical

requests are addressed (Q65). There is also an inter-

esting rule, the forth one: [Q29 = 4, Q60 = 1, Q97 =

4]− > [Q26 = 4][2.2% 97.0%]

there is a strong implication between the state of

the building (particularly the level – Q29), the price

(Q97) and the respondent’s expectations (Q60), in

that the expectations actually correspond, although

the price is considered too high.

2-Exception of

rs([Q97 = 4] → [Q26 = 4] [2% 95%])

The analyst might also want to check if there are

exceptions to the speciﬁed Rule Schema. With the

support threshold lowered to 1% (exceptions are rare

rules), a 2-Exception operation outputs:

15 results.

[S_boi, Q18=1, Q97=4] -> [Q26=1] [ 1.5% 100.0%]

[S_boi, Q29=1, Q97=4] -> [Q26=1] [ 1.5% 100.0%]

[S_boi, Q16=1, Q97=4] -> [Q26=1] [ 1.3% 100.0%]

[S_boi, Q47=1, Q97=4] -> [Q26=1] [ 1.2% 100.0%]

[S_boi, Q78=1, Q97=4] -> [Q26=1] [ 1.2% 100.0%]

[S_boi, Q92=1, Q97=4] -> [Q26=1] [ 1.0% 100.0%]

...

This result is very interesting. It shows that dissat-

isfaction in price can actually lead to a better opinion

on the building, if it is connected, among with some

other items, with a certain neighbourhood (Boissiere),

which is also very calm (Q8). This is important to

know, because, although clients are quite happy with

the conditions in that neighbourhood, they are un-

happy with the price.

2-Generalization of

rs([S boi, Q18 = 1, Q97 = 4] → [Q26 = 1])

Considering the last discovery in 2-Exception, the

analyst might want to look a bit more into the ﬁrst

rule, so he performs a 2-Generalization, with the fol-

lowing output:

3 results.

[S_boi] -> [Q26=1] [ 5.0% 88.1% ]

[Q97=4] -> [Q26=1] [ 22.5% 59.1% ]

[Q18=1] -> [Q26=1] [ 63.2% 76.2% ]

Indeed, the Boissiere neighbourhood has a great

inﬂuence on the satisfaction about state of the hall

(Q26).

Conﬁrmation of

rs([S boi] → [Q26 = 1] [Q92]) [1% 60%]

Last, the analyst will want to investigate the rela-

tion of the ﬁrst rule in the last result with the satisfac-

tion about adequacy of Nantes Habitat services (Q92).

The analyst performs a Conﬁrmation operation on the

Rule Schema.

There are 2 results:

[S_boi, Q92=1] -> [Q26=1] [ 3.7% 88.7% ]

[S_boi] -> [Q26=1, Q92=1] [ 3.7% 65.4% ]

It appears that the rules relate more to the satis-

faction answer to Question 92 (Q92

1) and it is usu-

ally perceived by the customers in this neighbourhood

ICEIS 2009 - International Conference on Enterprise Information Systems

204

that a good adequacy of the agency’s services leads,

among others, to a good state of the building.

This is only an example of actions that an analyst

may perform. Using Rule Schemas is easy and al-

lows focus on the most interesting rules. Also, the

operations are executed very quickly. For compari-

son, using apriori and rule ﬁltering would require ex-

tracting all the rules in the database and ﬁltering all

of them every time (1.528.978 rules for 8% of sup-

port and 85% of conﬁdence), in order to obtain the

desired results. Moreover, if the database is more dy-

namic, the rule extraction must be done again, which

can take a considerable amount of time.

5 CONCLUSIONS

In this paper we have presented a new solution for

local association rule mining that integrates user be-

liefs and expectations. The solution has two impor-

tant components. The Rule Schema formalism, based

on the concepts introduced by Liu (Liu et al., 1999),

helps the user focus the search for interesting rules, by

means of a ﬂexible and unitary manner of representa-

tion. The local mining algorithm that was developed

does not extract all rules and then post-process them,

but, instead, searches interesting rules in the vicinity

of what the user believes or expects. This way, the

user can explore the rule space in a local and incre-

mental manner, global processing being avoided.

The proposed algorithm was tested on a real-life

example, showing that the presented solution is valid

and leads to good practical results.

REFERENCES

Agrawal, R., Imielinski, T., and Swami, A. (1993). Min-

ing association rules between sets of items in large

databases. ACM SIGMOD Record, 22(2):207–216.

Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and

Verkamo, A. I. (1996). Fast discovery of association

rules. Advances in knowledge discovery and data min-

ing, 1:307–328.

Anand, S. S., Bell, D. A., and Hughes, J. G. (1995). The role

of domain knowledge in data mining. Proceedings

of the fourth international conference on Information

and knowledge management, 1:37–43.

Bayardo, R. J., Bayardo, R. J., Agrawal, R., Agrawal,

R., Gunopulos, D., and Gunopulos, D. (1999).

Constraint-based rule mining in large, dense

databases. In Proceedings of the 15th Interna-

tional Conference on Data Engineering, pages

188–197.

Blanchard, J., Guillet, F., and Briand, H. (2007). Interac-

tive visual exploration of association rules with rule-

focusing methodology. Knowledge and Information

Systems, 13:43–75.

Ceglar, A. and Roddick, J. F. (2006). Association mining.

ACM Comput. Surv., 38(2):5.

Duval, B., Salleb, A., and Vrain, C. (2007). On the discov-

ery of exception rules: A survey. Quality Measures in

Data Mining, pages 77–98.

Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P.

(1996). From data mining to knowledge discovery:

An overview. Advances in Knowledge Discovery and

Data Mining, 1:1 –34.

Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen,

H., and Verkamo, A. I. (1994). Finding interesting

rules from large sets of discovered association rules.

Proceedings of the third international conference on

Information and knowledge management, pages 401–

407.

Li, J. (2006). On optimal rule discovery. IEEE Transactions

on Knowledge and Data Engineering, 18(4):460–471.

Liu, B., Hsu, W., and Chen, S. (1997). Using general im-

pressions to analyze discovered classiﬁcation rules.

Proc. 3rd Int. Conf. Knowledge Discovery & Data

Mining, 1:31–36.

Liu, B., Hsu, W., Wang, K., and Chen, S. (1999). Visu-

ally aided exploration of interesting association rules.

PAKDD ’99: Proceedings of the Third Paciﬁc-Asia

Conference on Methodologies for Knowledge Discov-

ery and Data Mining, pages 380–389.

Padmanabhan, B. and Tuzhilin, A. (1998). A belief-driven

method for discovering unexpected patterns. Proceed-

ings of the Fourth International Conference on Knowl-

edge Discovery and Data Mining, 1:94–100.

Pei, J. and Han, J. (2000). Mining frequent patterns by

pattern-growth: methodology and implications. ACM

SIGKDD Explorations, 2:14–20.

Phillips, J. and Buchanan, B. G. (2001). Ontology-guided

knowledge discovery in databases. Proceedings of

the international conference on Knowledge capture,

pages 123–130.

Piatetsky-Shapiro, G. and Matheus, C. J. (1994). The in-

terestingness of deviations. Proceedings of the AAAI-

94 Workshop on Knowledge Discovery in Databases,

1:25–36.

Silberschatz, A. and Tuzhilin, A. (1995). On subjective

measures of interestingness in knowledge discovery.

Proceedings of the First International Conference on

Knowledge Discovery and Data Mining, pages 275–

281.

Srikant, R. and Agrawal, R. (1995). Mining generalized as-

sociation rules. Future Generation Computer Systems,

13:161–180.

USER-DRIVEN ASSOCIATION RULE MINING USING A LOCAL ALGORITHM

205