Apriori Algorithm for Frequent Pattern Mining for
Public Librariesin United States
Muhammad Muhajir, Ayundyah Kesumawati, Satibi Mulyadi
Departement of Statistics, Faculty of Mathematics and Natural Science, Islamic University of Indonesia
Keywords: Data Mining, Apriori, Frequent Pattern Mining, Map.
Abstract: This paper shows how the different approaches achieve the objective of frequent mining along with the
complexities required to perform the job and also give the descriptive by map representation about the
number of public library in United States. In addition, we demonstrate the use of Big ML tool for
association rule mining using Apriori algorithm. There are three attributes of public libraries is used among
other City, Location type, and State. The result shows that there are six most recommended rules with
confidence value ≥ 0.8 that are, Rules {City includes Chicago} => {Location type=Branch library}; Rules
{City includes Brooklyn} => {Location type=Branch library}; Rules {City includes Losangeles} =>
{Location type=Branch library}; Rules {City includes Baltiomore} => {Location type=Branch library};
Rules {City includes Sandiego} => {Location type=Branch library} and Rules {City includes Miami} =>
{Location type=Branch library}. The six rules means that the most visited public libraries by population of
legal service area over one million people is a branch library.
1 INTRODUCTION
One of service that could be accessed by the general
public is public library, where its management is
done by the librarian and professional library. There
are some of the characteristics of public libraries
among others: library funded by the community
through taxes, the public can access library
collection, library service provided to the public free
of charge. The main objective of public libraries is
prepared to meet the needs of individuals and group
in education, provide information resources and
services to the public (Rubin, 2010).
The development of information currently
provides a huge impact for librarian. The librarian
are required to follow every the development of
information that was going on , so information about
who gained may applied in management of libraries.
librarian is has a lot of digging pieces of data
derived from human activity from various sources.
The data has a very large and widely in terms of
quantity , variatif and acceleration. The term
massive data can be called as big data that changed
the way human understanding the world that had a
huge impact and will keep creating ripples through
all aspect of human life (Nath, 2015)
The united states was one of the countries
establish technology the big data in management of
systems public libraries. However, there are the
problems faced by public libraries there which is
approximately 80 % out of a total of public libraries
available data could only present serving 25,000 a
person from year to year, while the rest would
belong serve around 1,000,000 a person from year to
year (ALA Library, 2014).
Increase in the information or data , we need to
proper management that public library have its
existence as providers information that updates and
easily accessible. Hence , the application of the big
data concept in public libraries should be introduced
that data continues to grow library can be set
systematically. Data mining is technology that is
very useful for assist public library could find
important information from the data the warehouse.
One of the methods to find knowledge in a relational
large database is association rules. (Agrawal et al.
1993).
Here we have discussed the mechanism for Big
ML to use Apriori algorithm in Public Libraries
United States. Due to some problems that mentioned
before, the problems which will be discussed is to
mapping the type of libraries that most visited in
United States. Before do the mapping, the libraries
60
Muhajir, M., Kesumawati, A. and Mulyadi, S.
Apriori Algorithm for Frequent Pattern Mining for Public Librariesin United States.
DOI: 10.5220/0008517200600064
In Proceedings of the International Conference on Mathematics and Islam (ICMIs 2018), pages 60-64
ISBN: 978-989-758-407-7
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
in United States will be clustered in order to ease the
process of mapping. The expected benefit of writing
this paper is to know the types of libraries that most
visited in United States.
2 METHOD
2.1 Association Rule
Association rule mining is a technique data
mining to find rules associative between a
combination items. One of the stages in association
analysis that attracts attention many researchers to
produce the efficient algorithms is frequent pattern
mining (Jiawei, and Micheline, 2006). Support is
presentation combination the items in a database,
where if have items X and items Y so support is the
proportion of transactions in a database that are
containing X and Y (Han, and Kamber, 2006).
Definition 1. The value of an item support
calculated as (Srikant, 1996):
.
Confidence is the size of the accuracy of a rule,
the proportion of transactions in a database
containing containing X and Y. Confidence can also
measure the strength of the relationship between the
items in association rule
Definition 2. The value confidence than two items
are as follows (Srikant, 1996):
,
where P
: conditional probability of occurrence
of Y when X events occurred,
: probability
of occurrence of X and Y simultaneously, :
probability of occurrence X.
The lift ratio is a measure to analyze the strength of
association rules formed. The value of lift ratio
usually used as a determining whether the
association rules valid or invalid.
Definition 3. To calculate the lift ratio, we used the
formula as follows (Srikant, 1996):
,
Association rule mining can be broken down into
the following two subproblems among others
first,generating all itemsets that have support greater
than, or equal to, user specified minimum support;
second, generating all rules that have minimum
confidence (Brin et al., 1997).
2.2 The Apriori Algorithm
Apriori is popular algorithm and much used for
identification mining frequent itemsets. Apriori
algorithm steps described in the flow diagram the
following (Agarwal et al., 1993):
Figure 1: Flowchart of the Apriori Algorithm.
From Figure 1, the first step in association rule
mining is to find frequent itemsets. K-itemset is
defined as an itemsetas k items, Lk as the set of
frequent k-itemsets, and Ck as the set of candidate k-
itemset. Pseude-code following a priori algorithm
used to generate all frequent itemsets and pruning
frequent itemset unattractive in a transaction data
base.
2.3 Geographic Information System
Geographic information system or information
system based mapping and geography is
management of information closely related to a
Apriori Algorithm for Frequent Pattern Mining for Public Librariesin United States
61
mapping system. GIS technology integrating the
operation of a database that are used today, as the
collection of data, as well as statistic analysis with
visualization. There are two kinds of data in
geographic information system i.e. the data
geospasial or usually called spatial data and non
spatial (attributes) data (Berry, 1993).
3 EXPERIMENTAL RESULT
3.1 Desriptive
First, we identification the data with descriptive
analysis using by using mapping, we can see for the
distribution number of public libraries as follows:
Figure 2: Distribution of Public Libraries in US
There are five kind of location type in Public
Libraries is Bookmobile type, Books by mail type,
Branch library, Central Library, and library system
type. From the Figure 2, we can conclude that the
spread of public libraries in the eastern US state of
New York that region and surrounding areas.
3.2 Association Rules Apriori
Algorithm
The result of the analysis using Big ML software can
be shown in table 1. Based on this table, the
minimum support for apriori algorithm is 0.0830 and
the minimum confidence 0.2920. From these values,
we can analyze the assosiation rules between three
atrributes of public libraries data.
Based on table 1, all of the confidence values is
greater than 0.8 for six association pattern are
formed i.e.
a. Rule{City includes Chicago} => {Location
type=Branch library}
Rules with support value is 0.0029, confidence is
0.9625 and lift ratio is 3.4006. It’s mean that
0.29% or about 77 Branch library of the whole
location type happen in Chicago. Confidence
value means that in case of Chicago is going to
have Branch library with a confidence level of
96.25%. From the lift ratio value, we can see
how strong the rule formed by the apriori pattern
mining algorithms. The value of lift ratio is
positive (≥ 0). If the lift ratio value equals to 1,
then the rule {City includes Chicago} =>
{Location type=Branch library} often occurred
together but independently.
Table 1: Result of a rules with Apriori Algorithm using
BIG ML software.
Rules
City
Location
Type
Support
Confi-
dence
Lift
Ratio
1
Chicago
Branch
Library
0.0029
0.9625
3.4006
2
Brooklyn
Branch
Library
0.0023
0.8857
3.1293
3
Losangeles
Branch
Library
0.0021
0.9655
3.4113
4
Baltimore
Branch
Library
0.0012
0.9167
3.2387
5
Sandiego
Branch
Library
0.0013
0.8293
2.9299
6
Miami
Branch
Library
0.0001
0.8000
2.8265
b. Rule{City includes Brooklyn} => {Location
type=Branch library}
Rules with support value is 0.0023, confidence is
0.8857 and lift ratio is 3.1293. It’s mean that
0.23% or about 61 Branch library of the whole
location type happen in Brooklyn, Brooklyn is
going to have Branch library with a confidence
level of 88.571%, and lift ratio value is 3.1293.
From the lift ratio value, we can see how strong
the rule formed by the apriori pattern mining
algorithms. The value of lift ratio is positive (≥
0). If the lift ratio value equals to 1, then the rule,
then the rule {City includes Brooklyn} =>
{Location type=Branch library} often occurred
together but independently.
c. Rule{City includes Losangeles} => {Location
type=Branch library}
Rules with support value is 0.0021, confidence is
0.9655 and lift ratio is 3.4113. It’s mean that
0.21% or about 55 Branch library of the whole
location type happen in Losangeles, Losangeles
ICMIs 2018 - International Conference on Mathematics and Islam
62
is going to have Branch library with a confidence
level of 96.55%, and lift ratio value is 3.4113.
From the lift ratio value, we can see how strong
the rule formed by the apriori pattern mining
algorithms. The value of lift ratio is positive (≥
0). If the lift ratio value equals to 1, then the rule
{City includes Losangeles} => {Location
type=Branch library} often occurred together but
independently.
d. Rule{City includes Baltimore} => {Location
type=Branch library}
Rules with support value is 0.0012, confidence is
0.9167 and lift ratio is 3.`2387. It’s mean that
0.12% or about 33 Branch library of the whole
location type happen in Baltimore, Baltimore is
going to have Branch library with a confidence
level of 91.67%, and lift ratio value of 3.2387.
From the lift ratio value, we can see how strong
the rule formed by the apriori pattern mining
algorithms. The value of lift ratio is positive (≥
0). If the lift ratio value equals to 1, then the rule
{City includes Baltimore} => {Location
type=Branch library} often occurred together but
independently.
e. Rule{City includes Sandiego} => {Location
type=Branch library }
Rules with support value is 0.0013, confidence is
0.8293 and lift ratio is 2.9299. It’s mean that
0.13% or about 33 Branch library of the whole
location type happen in Sandiego, Sandiego is
going to have Branch library with a confidence
level of 91.67%, and lift ratio value of 3.2387.
From the lift ratio value, we can see how strong
the rule formed by the apriori pattern mining
algorithms. The value of lift ratio is positive (≥
0). If the lift ratio value equals to 1, then the rule
{City includes Sandiego} => {Location
type=Branch library} often occurred together but
independently.
f. Rule{City includes Miami} => {Location
type=Branch library}
Rules with support value is 0.0001, confidence is
0.80 and lift ratio is 2.8265. It’s mean that 0.1%
or about 27 Branch library of the whole location
type happen in Miami, Miami is going to have
Branch library with a confidence level of
91.67%, and lift ratio value of 3.2387. From the
lift ratio value, we can see how strong the rule
formed by the apriori pattern mining algorithms.
The value of lift ratio is positive (≥ 0). If the lift
ratio value equals to 1, then the rule {City
includes Miami} => {Location type=Branch
library} often occurred together but
independently.
4 CONCLUSION
The results of association rules analysis can be
concluded that the most recommended rules are
Rules{City includes Chicago} => {Location
type=Branch library}; Rules{City includes
Brooklyn} => {Location type=Branch library};
Rules{City includes Losangeles} => {Location
type=Branch library}; Rules{City includes
Baltiomore} => {Location type=Branch library};
Rules{City includes Sandiego} => {Location
type=Branch library}; Rules{City includes Miami}
=> {Location type=Branch library}. From the six
rules mentioned above, we known that the most
visited library in these six states in US is Branch
Library. It’s because the location of the Branch
Library usually near to people’s houses. Therefore,
to be visited by more people, the main branch library
should be developed near to the people’s house.
Management of other types of libraries should refer
to branch library management. It’s shown by the
result of the association rules analysis, which
confirmed that the Branch Library is the library that
most visited by population in that area.
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