Survey on Association Rule Mining Algorithms in
Medical Domain
Archana Sasi, Vidhu Kiran G & Sony P
Dept. of Computer Science
College of Engineering Cherthala
Cherthala, India
archana.sasi2k8@gmail.com , vidhukiran5690@gmail.com,
spsony@gmail.com
Abstract— Medical field is an ever growing and evolving
field of study in which lot of researches and findings take
place. The medical knowledge discovery from databases can
play a vital role in effective medical diagnosis. Availability
and extraction of useful medical information is a crucial
factor in this field. The association rule mining, one of the
data mining technique helps in finding relevant relationships
among various terms present in diverse documents. Over these
years many ideas have been put forward for the generation of
itemsets. In these ideas, the concern is on how efficiently such
algorithms can generate these frequent itemsets. This study, is
an analysis of such various frequent itemsets generating
algorithms which are useful in medical domain. The majority
of medical data is static in nature and thus the concerned
algorithms need only to handle static data. The study
overlooks few such algorithms that efficiently handle the
medical data which includes Apriori, DHP, Eclat, FP-growth
etc.
Keywords—Association Rule, Apriori, DHP, Eclat, Frequent
Itemsets mining, , FP-growth
I.
INTRODUCTION
Recently there has been a drastic increase in the size of
databases at a great rate. This has led to a growing interest in
the development of tools capable in the automatic extraction
of knowledge from data [6]. Data mining is a collection of
techniques for efficient automated discovery of previously
unknown patterns and associations, by automatically
extracting information within the databases, which can be
either semi-structured or unstructured.
The issue of mining frequent itemsets arose first as a
sub-problem of mining association rules. Frequent itemsets
play a vital role in many data mining applications that try to
find interesting patterns from databases such as association
rules [6], clusters, classifiers, correlations and many more.
Since the evolution of life on earth, there had been lots of
inventions going on in the field of medicine. For helping
biomedical researchers, lots of efficient and important
resources like MEDLINE, PubMed, SNOMED CT, etc. are
available today. MEDLINE [7] includes its citations and
summary of biomedical information’s from the world. The
PubMed [7] provides free access to the MEDLINE[15]
database of references and abstracts on biomedical
informations. SNOMED CT[16] offers codes, synonyms,
terms and definitions from collection of medical term which
are systematically organized and processable by computers[8].
Along with these inventions, the trends in information
technology are arising. So it is essential to produce relevant
association rules between medical terms in medical
documents. In this study, we make use of a domain ontology
called Unified Medical Language System (UMLS) to
recognize medical terms. These documents are mined to
produce association rules between medical terms such as
disease, medications, symptoms etc.
Here, we explain the basic frequent itemset and
association rule mining problems that occur in the medical
data sets. We also describe the main techniques used to solve
these problems.
II.
MEDICAL DATA FORMATS
Clinical Medical Records contains plenty of information that
can prove indispensable for the performance of clinical
research. These reports are preserved in free textual form.
Some such reports include Electronic Medical Records
(EMR), Electronic Health Records (EHR), Electronic Patient
Records (EPR). Though these terms are often used
interchangeably, they can be defined differently. EMR is a
methodical group of electronic health information about a
single patient or population. It is the digital format of the
patient records created at hospitals and similar environments
[1]. This can serve as a data source for the EHR. Whereas
EHR is a systematic collections of electronic information
generated and maintained within an institution like hospitals
for accessing patient’s medical records across amenities. EPR
is an organizational record of treatment generated when a
patient is treated. Since the medical data available are not
always structured hence pre-processing of these data is
essential to improve the number of inconsistencies associated
with data and also to improve the quality of data [9]. The
medical reports available are either unstructured or semistructured. So it is important to convert them into structured
format before applying association rule mining algorithms.
The data set in concern here is commonly known as
the Electronic Medical Reports (EMR)[9]. These are actually
medical reports that are stored in the electronic file format.
The EMRs normally follow a defined structure. If the EMRs
are properly structured then the application of association
rules is much easier task to dealt with. At the same time it
becomes very difficult to analyze when EMR is not in proper
structure [1]. Hence it is of great importance to obtain a
medical dataset in structured format. Although mentioned that
structured medical reports are required for the efficient data
mining it’s a far cry and not yet completely achievable. The
availability of good structured medical reports and that too in
bulk amount is not feasible due to current situations. This is
because generating and running clinical reports represents a
major price to the health care enterprise. So rather than going
for the completely structured data sets, it is easier to use the
semi-structured medical reports which are easily available.
mining techniques used in medical domain include decision
tree algorithms[18] such as ID3[19], C4.5 and CART for
finding decision support in medical databases[17][18], outlier
prediction technique for improving classification accuracy[9],
combined use of K-means, SOM, and Naïve Bayes for
accurate classification of medical data[12], Fuzzy Cognitive
Maps[11] for classification of drugs and health effects etc.
Data mining applications can deeply helps all parties involved
in the Medical Field. For example, data mining can help
Medical Field insurers to spot fraud and mistreatment,
physicians recognize effective treatments and best practices,
and patients get better and additional affordable Medical Field
services. The vast amount of data generated by Medical Field
transactions are very difficult and huge to be processed and
analyzed by conventional methods. Data mining provides the
technologies and methods to vary these mass of data into
helpful information for decision making [9]. .Data mining
applications can be developed to estimate the effectiveness of
medical treatments. By examining the causes, symptoms, and
modes of treatments, data mining can fetch an examination of
which courses of action prove efficient.
IV.
III.
TEXT MINING IN MEDICAL FIELD
With the advancement in the web, methodological
documentations, digital libraries, medical data etc, the access
to a huge amount of textual documents becomes more capable
[5]. The abundance and uncertainty of natural language makes
the text mining more challenging. The mining and
examination of useful information from those documents
written in natural language is very hard in nature[6][8].
Knowledge Discovery from Databases (KDD) denotes
extracting interesting and non-trivial relations and patterns
from data in large databases[8]. Data mining activities mainly
consists of description and visualizations, finding association
between data elements, clustering of similar records,
classification of data[9], regression, prediction based on trends
that can be extracted from data etc,[18]. Medical field has
enough sources to generate large and dynamic data
warehouses which are hard to analyze by professionals
without the aid of computers [4]. There are numerous well
established data mining techniques in the medical field
concerning healthcare management, evaluating effectiveness
of treatments, clinical trials, electronic patient records, etc..
Data mining approaches such as clustering, data classification,
regression, association rule mining, CART are widely used in
health care domain [9]. In spite of the clear benefits, there
exist a lot of limitations for data mining analysis techniques.
Due to the distribution of data in different settings, the
accessibility of data becomes difficult. The available Data may
be imperfect, incomplete degraded or inconsistent [18].
Data mining techniques have shown significant improvement
in medical industry in terms of prediction and decision making
with respect to various diseases like cancer, renal
abnormalities, diabetes and others [9]. Some of the data
ROLE OF ONTOLOGY IN MEDICAL
DOMAIN
In recent years various ontologies have been developed in
biomedical field with an intend to represent biomedical terms
in a common vocabulary [16]. Some of thel ontologies used in
biomedical domains include OpenCyc, WordNet[10],
GALEN, UMLS, SNOMED-CT[8], FMA, Geneontology etc.
Since, most of the medical terms are multi-word
phrases, it is not easier to explore all combinations of
sequential words in the sentence from a fixed domain ontology
[5][8]. Rather, we use POS tagger and then employ an ordered
patterns to get a list of candidate terms and then see if they
exist in the ontology [5][6]. Here, in this study we are using a
medical ontology called Unified Medical Language System
(UMLS) produced by the United States National Library Of
Medicine (NLM)[20]. UMLS has been customized using
Metamorphosys which contains a huge number of
vocabularies that defines vast number of medical terms.
Concept Unique Identifier (CUI) present in one of the data file
of UMLS called MRCONSO is used to discover the semantic
type of the term [22].
One of the UMLS Knowledge Sources called The
SPECIALIST Lexicon provides the word usage information
needed for the SPECIALIST NLP system. These system tools
are designed to help users manage lexical variation in
biomedical text. A range of relations that keep among lexemes
and their senses include Homonymy, Hyponymy, Hypernymy,
Polysemy, Meronymy etc[21]. Homonymy is defined as a
relation that holds between words that have the same form
with unrelated meanings. Hypernymy is the semantic relation
in which one word is the hypernym of another. It shares a kind
of is a relation, whereas hyponymy is the converse relation of
hypernymy. A meronymy is a semantic relation that represents
constituent part of whole.
Finally, if a term is present in the UMLS database,
we then keep it and continue to search for other terms from
current position.
V. PROBLEM STUDY
Advancements in digital libraries, medical data, web, technical
documentation has all resulted in the availability of large
amount of textual documents [5]. All these textual data
comprises of useful and important information which is worth
harnessing. Manual analysis and effective extraction of this
information is not feasible. So it is essential to discover
valuable relationships between the various in the textual
documents. Association rule mining [10][24] helps to achieve
these important relationships.
Medical Diagnosis is one of the major beneficiaries of data
mining, especially association rule mining [3]. Even though
the medical sector is loaded with lots of information but still
the knowledge that we can attain from this information is very
limited and of poor quality. The data mining applications can
considerably change the situation. The general diagnosis
process normally involves a lot of hypothetical and
probabilistic assumptions .It is by using association rules that
these assumptions can be turned into a pattern which there by
assists physicians in easier disease diagnosis and accurate
treatments.
The task of mining frequent itemsets[16] arose first as a
sub-problem of mining association rules, but this task is
somewhat challenging. The search space is exponential in the
number of terms occurring in the database. Also, such
databases could be massive, involving millions of documents,
making counting support a tedious task.
To compute the supports of a collection of itemsets, we need
to access the database. An important consideration in most
algorithms is the representation of the document database [5].
The two most commonly used layout for representing database
matrix are horizontal data layout and vertical data layout.
Breadth First Search Algorithms uses horizontal data layout
and Depth First Search Algorithms uses vertical data layout
[23].
A. Breadth-First Search Algorithms
A1. Apriori Algorithm
One of the most popular algorithm that actually solve
the complete association rule mining problem is called Apriori
algorithm [4]. Since the frequent set mining is a sub-problem
in association rule mining phase, this frequent itemset mining
is somewhat a difficult phase in Apriori algorithm [18].
The algorithm performs a breadth-first search
approach through the search space of all sets by iteratively
generating and counting a group of candidate sets [4]. An
itemset can be treated as a candidate if all of its subsets are
frequent. Since frequent itemset mining is a challenging phase
in this algorithm, after finding all frequent itemsets, we can
easily generate the confident association rule.
It seems that this algorithm does not fully exploit the
monotonicity of confidence [13]. Fortunately, if the number of
frequent and confident association rules is not too big, then the
time required to find all such rules consists of the time that
was needed to find all frequent sets.
Optimizations
After the introduction of the Apriori algorithm, a
number of other algorithms were proposed, which keeps the
same general structure, but by adding certain techniques to
optimize some steps within the algorithm. Since the
performance of the this algorithm is completly enforced by the
procedure of counting support, most researches has
concentrated on that aspect. For counting the occurrences of
the candidate itemsets, there is no need to examine the whole
database each time. This reduces the time required to count the
support of candidate itemsets, and there by improves the
performance. Transaction pruning was present in the
following two algorithms: AprioriTid and DHP.
A2. AprioriTid, AprioriHybrid
Along with the proposal of the Apriori algorithm,
Agrawal et.al introduced two new algorithms, AprioriTid and
AprioriHybrid. The AprioriTid algorithm reduces the time
required for the support counting procedure. This is done by
replacing every document in the database by the set of
candidate itemsets that occur in that document. The main
difference from Apriori is that, it does not use the whole
database to count the support of candidate itemsets .
Although this algorithm is much faster in later
iterations, in early iterations it behaves much slowly.
Moreover, at later iterations, each entry may be lesser than the
corresponding document because very few candidates may be
present in that document. However, in early iterations, every
entry may be larger than its corresponding document. This
instigated the authors of the algorithm to propose another
algorithm that joins the Apriori and AprioriTid algorithms into
a single hybrid called AprioriHybrid. This algorithm uses
Apriori for the first stages and then switches to AprioriTid
when document pruning becomes more powerful. When to
switch between the algorithms is not much vivid, but the
authors showed that it can be resolved heuristically. However,
AprioriHybrid carryout almost always better than Apriori.
A3. DHP Algorithm
Direct Hashing and Pruning algorithm proposes
overcoming some of the drawbacks of the Apriori algorithm
by reducing the number of candidate k-itemsets, especially,
the 2-itemsets, since that is the key for improving the
performance. The DHP algorithm claims to be powerful in the
generation of frequent itemsets and effective in trimming the
document database by discarding terms from the documents or
eliminating the whole documents that need not to be scanned
.The algorithm uses a hash based technique to reduce the
number of candidate itemsets generated in the initial pass.
This algorithm may be partitioned into the following
three parts. The first part finds all the frequent 1-itemsets and
all the candidate 2-itemsets.The second part is the more
common part that includes hashing and the third without the
hashing. Both these parts include pruning. Part two is used for
early iterations and part three is used for later iterations.
One of the impacts on the effectiveness of the
algorithm is the reduction in the database. Because of this
efficient pruning of the database, the algorithm can attain a
significantly shorter execution time than the Apriori
algorithm.
B. Depth First Search Algorithms
B1. Eclat Algorithm
Eclat algorithm is essentially a depth–first
search algorithm. Instead of explicitly listing all documents[4]
, it uses a vertical database layout. That is, here each term is
stored together with its cover and in order to compute the
support of a itemset[5] it uses an intersection based approach .
The main advantage of using vertical data layout is that, the
support of a set can be computed much easier by intersecting
the covers of two of its subsets which together give the set
itself.
Here each frequent term generated is added in the
output set. Eclat algorithm doesn’t totally exploit the
monotonicity property[4] , but the number of candidate
itemsets that are generated is much larger as compared to the
breadth-first search algorithms. On comparing with the
previously defined algorithms it is obvious that, only by using
the join step from the Apriori, that the Eclat generates the
candidate itemsets. This is because the itemsets necessary for
the prune step are not available.
Again, to reduce the number of candidate itemsets generated
we can reorder all the terms in the document database in
ascending order of the support value. And thereby we can
reduce the number of intersections that wants to be calculated
and thus the total size of covers of all generated itemsets. It is
at every recursion step of the algorithm that this reordering is
actually performed .On comparing with Apriori, the supports
of all itemsets can be calculated more efficiently.
B2. FP-GROWTH
One of the most popular algorithm that mines
frequent patterns[14] without candidate generation is called
FP-Growth Algorithm. This algorithm uses an approach that is
entirely different from that used by methods based on Apriori
algorithm.
For storing database in main memory, FP-growth
uses a combination of the horizontal and vertical database
layout to compute the supports of all generated itemets[5]. To
remove the bottlenecks of the Apriori-Algorithm in generating
and testing candidate itemset was the main goal of this
algorithm. Instead of storing the cover for every term in the
database, it stores the actual documents from the database in a
tree structure. Here every term has a linked list going through
all documents that contain that term [3]. This new data
structure generated is denoted by FP-Tree or Frequent-Pattern
Tree [15]. The major difference between frequent patterngrowth (FP-Growth) and the other algorithms is that FPgrowth instead of generating the candidates itemsets, it only
tests. But in contrast, the Apriori algorithm generates the
candidate itemsets and then tests.
The motivation for the FP-tree method is as given below:
 To find the association rules, only the frequent terms
are required, so it best to find the frequent terms and
ignore others.
 If a compact structure is used to store the frequent
items, then the original document database does not
need to be used frequently.
The FP-growth algorithm is somewhat similar to Eclat
algorithm. To conserve the FP-tree structure it uses some
additional steps during the recursion step of Eclat. One
advantage of the FP-tree algorithm is that it ignores scanning
the database more than twice to calculate the support counts.
Another benefit of using this algorithm is that is that, it
completely eliminates the expensive candidate generation,
which can be costly in particular for the Apriori algorithm for
the candidate set C2.The FP-growth algorithm is better than
the Apriori algorithm when the document database is large and
the minimum support count is low. A low minimum support
count results a lot of terms to satisfy the support count and
hence the size of the candidate sets for Apriori becomes large.
FP-growth uses a more efficient structure to mine patterns
when the database grows.
The only difference between Eclat and FP-growth is the
method in which they count the supports of every candidate
itemset and how they represent and retain the i-projected
database.
VI. PERFORMANCE EVALUATION
The data set consists of 400 patient medical transcription
reports from the domain of kidney disease were collected for
the study. The report was in semi-structured format. The task
was to find the number of rules that contain Chronic Kidney
Disease. The general architecture of the system is shown
below:
PRECISION-RECALL GRAPH
The Input to the system consists of both structured and
semi-structured documents. In the initial phase these are
converted into structured documents. It is in the second phase
that the association rules are generated by first generating the
frequent itemsets using Éclat algorithm. Here, we used
precision and recall for evaluating the system performance.
The table below shows the precision and recall value
obtained for finding the association rules using above
mentioned algorithms and graph showing these values.
On analyzing these results it is clear that the number of rules
generated by depth first search algorithms always outperforms
breadth first search algorithms. Among the breath first search
algorithms Apriori Tid produce more number of rules.
Eventhough DHP performs good during initial stages, but due
to hash collision it generate less rules later. On comparing
depth first search algorithms Eclat produce more number of
rules than FP Growth.
VII. CONCLUSIONS
ARM
ALGORITHAMS
PRECISION
RECALL
APRIORI
83.98%
82.14%
APRIORI TID
85.11%
83.24%
DHP
79.16%
78.29%
FP GROWTH
88.45%
86.26%
ECLAT
91.34%
89.83%
The main aim of the paper was to verify various
algorithms that perform the association rule mining. Here the
medical data was subjected to association rule mining using
various algorithms. It was also observed that the result of
mining was different for different algorithms. Though their
variance was not by a huge factor as well, the result was based
on the performance of each algorithm on the same amount of
semi-structured medical reports. Hence it is also mentionable
that the results might vary accordingly to the type of medical
reports that is provided as input to these algorithms.
Here in this paper we presented the comparative
performance study of breath first search and depth first search
algorithms. This study includes a detailed analysis of these
algorithms, which contribute a great to the search of
improving the efficiency of frequent item set mining. This can
be also used for comparing the other algorithms which can
lead to some ideas for optimizations.
.
VIII. REFERENCES
[1]
Xu Yabin and Peng Hongyu “Disease Association Discovery
Based on XML EMR” Second International Wokshop on
Education Technology and Computer Science(ETCS) Volume 3,
March 2010, ISBN: 978-1-4244-6389-3
[2]
Patil B.M ,Joshi R.C and Toshniwal D “Association Rule for
classification of Type-2 Diabetic Patients”, Second IEEE
International Conference on Machine Learning and Computing
(ICMLC), Feb 2010, ISBN- 978-1-4244-6007-6
[3]
Xiaohua Zhou, Hyoil Han, Isaac Chankai, Ann A. Prestrud and Ari D.
Brooks “Converting Semi-structured Clinical Medical Records into
Information and Knowledge” Proceedings of the 21st International
Conference on Data Engineering , April 2005, ISBN: 0-7695-2657-8
Tianxia Gong, Chew Lim Tan, Tze Yun Leong, Cheng Kiang Lee, Boon
Chuan Pang, C. C. Tchoyoson Lim, Qi Tian, Suisheng Tang, Zhuo
Zhang “Text Mining in Radiology Reports” Eighth IEEE International
Conference on Data Mining, 2008, ISSN: 1550-4786
[4]
[5]
Hany Mahgoub, Dietmar Rosner, Nabil Ismail and Fawzy
Torkey “A Text Mining Technique Using Association Rules
Extraction” International Journal Of Information And
Mathematical Sciences, 2008, ISBN: 978-1-4577-2033-8
[6]
Vaishali Bhujade and N.J Janwe, “Knowledge Discovery in Text
Mining Technique Using Association Rules Extraction”, International
Conference on Computational Intelligence
and Communiaction
Networks, Oct 2011, ISBN: 978-1-4577-2033-8
K. Bretonnel Cohen, Wendy W. Chapman, “Current issues in
biomedical text mining and natural language processing” in
Journal of Biomedical Informatics 42 (2009)
[8] Tasha R.Inniss, John R.Lee, Mare Light, Michael
A.Grassi,George Thomas, Andrew B.Williams,”Towardds
Applying Text Mining and Natural Language Processing for
Biomedical Ontology Acquisition”, Proceedings of the First
International Workshop on Text Mining in Bioinformatics,
ISBN:-1-59593-526-6
[7]
[9]
Vili Podgorelec, Marjan HerikoMaribor, (n.d).” Improving Mining of
Medical Data by Outliers Prediction”, Proceedings of 18th IEEE
Symposium on June 2005,ISSN: 1063-7125
[10] George A. Miller, Richard Beckwith, Christiane Fellbaum,
Derek Gross, and Katherine Miller “Introduction to WordNet:
An On-line Lexical Database” 2010 Second International
Conference on Machine Learning and Computing
[11] Wojciech Froelich, Alicja Wakulicz-Deja (2009).,” Mining Temporal
Medical Data Using Adaptive Fuzzy Cognitive Maps.” Human System
Interactions,2009 HIS’09 .2nd Conference on May 2009, E-ISBN: 9781-4244-3960-7
[12] Syed Zahid Hassan and Brijesh Verma,” A Hybrid Data Mining
Approach for Knowledge Extraction and Classification in Medical
Databases” IEEE Seventh International Conference on Intelligent
Systems Design and Applications, Oct 2007, ISBN: 978-0-7695-2976-9
[13] Umair Abdullah (2008).” Analysis of Effectiveness of Apriori
Algorithm in Medical Billing Data Mining”. ICET 2008 Fourth
International Conference, Oct 2008,E-ISBN: 978-1-4244-2211-1
[14] Xiaoyong Lin and Qunxiong Zhu. (2010). “Share-Inherit: A novel
approach for mining frequent patterns”, IEEE Eigth world Congress on
Intelligent Control and Automation (WCICA), July 2010, ISBN: 978-14244-6712-9
[15] Carson Kai-Sang Leung,Christopher L. Carmichael and Boyu Hao.
(2007). “Efficient Mining of Frequent Patterns from Uncertain Data”.
Seventh IEEE International Conference on Data Mining Workshops
(ICDM),ct 2007, E-ISBN: 978-0-7695-3033-8
[16] Thanh-Trung Nguyen. (2010). “An Improved Algorithm for Frequent
Patterns Mining Problem”, International Symposium on Computer
Communication Control and Automation, May 2010, ISBN: 978-14244-5565-2
[17] Jenn-Lung Su, Guo-Zhen Wu, I-Pin Chao (2001). “The Approach
Of Data Mining Methods For Medical Database”.,.Proceedings of the
23rd Annual International Conference of the IEEE on Engineering in
Medicine and Biology Society, Vol 4, 2001, ISSN: 1094-687X
[18] Sam Chao, Fai Wong, “An Incremental Decision Tree Learning
Methodology regarding Attributes In Medical Data Mining”.
Proceedings of the Eighth International Conference on
Machine Learning and Cybernetics, Baoding, 12-15 July 2009,
E-ISBN: 978-1-4244-3703-0
[19] Lehnert,W.,Soderland S., Aronow, ., Feng, F., and Shmueli,A., “Inuctive
Text Classification for Medical Applications”, Journal for Experimental
and Theoretical Artificial Intelligence, 1994, 7(1),pp,49-80.
[20] UMLS Reference Manual – Bethesda (MD): National Library of
Medicine September 2009.
[21] Danushka Bollegala, Yutaka Matsuo and Mitusuru Ishizuka,”A
Relational Model of Semantic Similarity between Words using
Automatically Extracted Lexical Pattern Clusters from the web”.
Proceedings of the 2009 Conference on Empirical Methods in Natural
Language Processing
[22] Alan R.Aronson,”Effective Mapping of Biomedical Text to the UMLS
Metathesaurus: The MetaMap Program”. In:Proeedings of the American
Medical Informatics Association (AMIA) Symposium ;2001.p.17-21
[23] Jochen Hipp, Ulrich G¨untzer and Gholamreza,” Algorithms for
Association Rule Mining – A General Survey and Comparison”. ACM
SIGKDD, July 2000 Volume 2
[24] Haiwei Pan, Qilong Han, Guishng Yin and Wei Zhang“Association rule
mining with domain knowledge constraint”,Third International
Conference on Intelligent System and Knowledge Engineering
Volume:1, Nov 2008, E-ISBN: 978-1-4244-2197-8
[25] R.K.Taira, V.Bashyam and H.Kangarloo,”A field theoretical approach to
medical natural language processing.IEEE transaction on Information
Technology in Biomedicine,11(4):July 2007, ISSN: 1089-7771
[26] Li-Shiang T, Seunghyun I “Mining Generalized Actionable Rules
Using Concept Hierarchies”, Fifth International Joint Conference on .
INC, IMS and IDC, August 2009,E-ISBN: 978-0-7695-3769-6