Improved Navigation Pattern By Association Rule Mining

Improved Navigation Pattern By Association Rule Mining
Improved Navigation Pattern by Association Rule Mining
Chitresh Lodhi1, Kaushal K. Sharma2, Akshay Gupta3 123Oriental College of Technology, Patel
Nagar Raisen Road, Bhopal (M.P)
Correspondence to:
1singhchitresh36@gmail.com
2kaushalsharma400@gamil.com
3akshaygupta399@gmail.com
Abstract: Association rule mining can be used to extract patterns of a website visitors' behaviour.
This data can be used to improve web marketing (e–business) techniques or to improve the web
surfing experience. Here we are applying association rule on web usage log file of an institution. We
are using association rule as interesting measures and verifying their values in two different period
of time. We will see how this comparison brings extra important information about association rules
generation and helps a webmaster make more and more accurate decisions about the website
development and enhancements. Here our main aim is to remove user unwanted uninterested rules
and to replace them with new and most favourable patterns or we can say by association rules. In
this paper we are describing association rules mining approach.
Keywords: Web usage mining, association rules, interestingness measures, E –business.
Introduction
With the fast development of e–commerce (electronic commerce) sector in last few years, the
importance and applicability of intelligent data management techniques has become essential in the
e–business sector. The data about client behavior can play important role in
... Get more on HelpWriting.net ...

Closed Itemsets Mining Closed Itemset
2.4.1 A–Closed Algorithm Mining closed itemset, initially proposed in [23] Pasquier et al .A–close
algorithm is a basic algorithm in frequent closed itemsets mining which is based on Apriori
algorithm[25] in frequent item mining. A–close operation is performed in the following two general
steps: Producing frequent generators and achieving closure of frequent generators. For A–close
algorithm, an itemset p is generator of closed itemset y, if p is one of smallest itemsets (it may be
more than one), and it determines y with Galois closure operator h(p)=y [26]. To produce
generators, a level–wise approach, similar to that of Apriori algorithm is taken. Then three steps of
pruning are conducted on candidate generators, and useless generators are pruned thereby[26]. The
operation of generator production is repeated until no other generator is produced. After producing
generators G1 to Gn (n is maximum generator size), closure of all these frequent generators should
be computed. The closure of all frequent generators results in all closed frequent itemsets. The
technique for calculating closure as the next.
The closure of generator p which is achieved by applying function h from Relation (3) on p, is
intersection of all database transactions that include p . A–close algorithm uses breath–first–search
strategy for mining operation, and itemset lattice is analyzed in a bottom–up way. Its weak point is
the great number of candid items and passes from dataset, leading to much

A Report On The Data Mining
ABSTRACT
As with the development of the IT technologies , the amount of cumulative data is also Growing. It
has resulted large amount of data stock in databases , depot and other repositories . therefore the
Data mining comes into model to explore and analyses the databases to extract the interesting and
previously obscure patterns and rules well–known as association rule mining It was first introduced
in 1993.
In DM Association rule mining becomes one of the serious tasks of adjective technique which can
be defined as discovering important patterns from large collection of data. Mining frequent itemset
is very essential component of association rule mining.
The Frequent Pattern Tree " FP–Tree " algorithm is one of the most favored algorithms for
association rule mining because it gives association rules wanting generating candidate sets.
The whole set of association rules on FCI (frequent closed item sets) can be mined By using
CLOSET algorithm we can worthily mine the frequent closed item sets.
KEYWORDS
Data mining ; Association rules ; FP–tree ; FCI ; CLOSET
1. Introduction
With the increment in IT (Information Technology) the size of the databases generated by the
organizations due to the availability of low–cost store and the development in the data pick
technologies is also increasing. These organization sectors contain petroleum, telecommunications,
retail, utilities, transportation , manufacturing, credit cards , banking , insurance and many others,

Data Analysis : Data Mining
Introduction Data, Data everywhere. It is a precious thing that will last longer than the systems. In
this challenging world, there is a high demand to work efficiently without risk of losing any tiny
information which might be very important in future. Hence there is need to create large volumes of
data which needs to be stored and explored for future analysis. I am always fascinated to know how
this large amount of data is handled, stored in databases and manipulated to extract useful
information. A raw data is like an unpolished diamond, its value is known only after it is polished.
Similarly, the value of data is understood only after a proper meaning is brought out of it, this is
known as Data Mining.
Data Mining is a computer based–process for converting large data volumes to information and
knowledge by finding patterns within the data using different techniques. It is sorting through data
to identify patterns and establish relationships. Data mining helps resolving problems that are time
consuming when traditional techniques are used. Data mining techniques are used to predict future
trends and to make wise decisions. There are multiple Data Mining techniques available to the Data
diggers to make their life easy. In my study report I will be discussing about the different mining
techniques, advantages and disadvantages and also about a use case of the data mining techniques
on shark attack dataset to predict the attack of sharks based on various attributes.
Data

Is It Fast Distributed Mining? Essay
Abstract–Fast Distributed Mining (FDM) which generates a small number of candidate set and
substantially reduce the number of messages to be passed at mining association rules. Distributed
data mining offers a way by data can be shared without compromising privacy. The paper present
secure protocols for the task of top–k subgroup discovery on horizontally partitioned data. In this
setting, all sites use the same set of attributes and the quality of every subgroup depends on all
databases. The approach finds patterns in the union of the databases, without disclosing the local
databases. This is the first secure approach that tackles any of the supervised descriptive rule
discovery tasks. It is simpler and significantly more efficient in terms of communication rounds,
communication cost and computational cost.
Keywords – Frequent item set, Association rules, Security, Privacy
I. INTRODUCTION
F
ast Distributed Mining (FDM) is reduce the number of messages to be passed at mining association
rules. Mining encompasses various algorithms such as clustering, classification, association rule
mining and sequence detection. The goal is to find all association rules with support at least s and
confidence at least c, for some given minimal support size s and confidence level c, that hold in the
unified database, while minimizing the information disclosed about the private databases held by
those players. The information that we would like to protect in this context is not only

Notes On Data Mining And Association Rule Mining Essay
CHAPTER 2
2. Background and Literature Review
The purpose of this chapter is to show an in depth review of the topics, areas and works related to
the research presented here. we conduct a brief but comprehensive in depth review of data mining
and association rule mining approaches and techniques, followed by a focus at interestingness &
quality, and redundancy issues related to association rule mining. This review sets the basic work for
our research and the proposals made here.
2.1 Data mining
Data Mining technique is the result of a long process of studies and research in the area of databases
and product development. This evolution began when business data and companies was stored for
the first time on computer device, with continuous improvements in access to data and more newly,
produced technologies that allow users to navigate during their data in real time. Data mining is a
approach that help to mine important data from a large database. It is the technique of classification
during huge amounts of data and chosen out relevant information during the use of certain advanced
algorithms. Like more data is collected, with the amount of data doubling every one years, data
mining is becoming an more and more important tool to convert this data into information. Data
mining takes this evolutionary process behind retrospective data access and navigation to
prospective and proactive information delivery. Data mining is very useful and ready in applications
in the business

Secure Mining Of Association Rule With Subgroup Discovery...
SECURE MINING OF ASSOCIATION RULE WITH SUBGROUP DISCOVERY IN
HORIZONTALLY PARTITIONED DATA R.Loga Priya1, M.Madlin Asha2, Dr.M.Akila3
PG Scholar1, Department of Information Technology,
Assistant Professor2, Department of Information Technology, Professor3, Department of
Information Technology,
Vivekanandha College of Engineering for Women
Tiruchengode – 637205, India
Email: rlogapriyait@gmail.com1, madlinasha88.jesus@gmail.com2,akila@nvgroup.in3
Abstract–Fast Distributed Mining (FDM) which generates a small number of candidate set and
substantially reduce the number of messages to be passed at mining association rules. Distributed
data mining offers a way by data can be shared without compromising privacy. A protocol for secure
mining of association rules in horizontally distributed databases. The main ingredients in the
existing protocol are two novel secure multi–party algorithms–one that computes the union of
private subsets that each of the interacting players hold and another that tests the inclusion of an
element held by one player in a subset held by another. In order to improve the performance of the
system it presents the subgroup discovery concept in this system. The paper present secure protocols
for the task of top–k subgroup discovery on horizontally partitioned data. In this setting, all sites use
the same set of attributes and the quality of every subgroup depends on all databases. Subgroup
discovery is the task of finding subgroups of a

The Tenets Of Behaviorism
The term behaviorism referred to the school of psychology founded by John B. Watson based on the
proposition that all things which organisms do – including acting, thinking and feeling–can and
should be regarded as behaviors (Staddon, 2001) . And this behavior could be researched
scientifically . According to Pavlov, "Respondent Conditioning" resulted from the association of two
stimuli, such as causing dogs to salivate at the sound a tuning fork. Consequently, Skinner
developed "Operant Conditioning" where the "Stimulus–Response" association was elicited through
selective reinforcement (rewards or punishments) to shape behavior. In this regard, behaviorism
assumed that a learner was a passive recipient and responding to environmental stimuli. When
applying the tenets of Behaviorism to teaching, Skinner asserted that the learner started off as a
blank slate, and then his behavior was shaped via positive or negative reinforcement. Behaviorist ...
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It combined new thinking in psychology, anthropology and linguistics. As for cognitivism, learning
was seen as change in learner's schemata. Language came primarily though the maturation that the
environment triggered off and not through the environment itself. (Brown, H.D. & Gonzo, 1995).
Cognitive theorists may have wanted to understand how problem solving changed throughout
childhood, how cultural differences affected the way we view our own academic achievement ,
language development, and much more. (Feldman,1995). Unlike behaviourism, cognitivism
emphasized that learners were not a programmed animals or passive receivers that respond merely
to environmental stimuli; contrarily learners are rational human being and require active
participation to

A Norm Is A Rule Of Behavior Of Individuals In Society
A norm is a rule of behavior of individuals in society or groups, Emile Durkheim sociological
founder considered norms to be social factors that exist in individual's society of independence that
creates the shapes of our behaviors and thoughts (Crossman, 2017). In the terms of not having
sociality without social control the society would have no function without enforcing social orders
that make daily life and the complex division of labor possible (Crossman, 2017). Social order is
produced by ongoing life longs process of socialization that individual experience. Through the
process of stages of growing up when we are taught the norms, behavioral rules and appropriate
interaction that is common to our families. The process of how ... Show more content on
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Emile Durkheim developed the first mod ern strain theory of crime and deviance, but Merton's
classical strain of theory came to dominant criminology during the middle part of the 20th century
("Strain theories – criminology – Oxford bibliographies – obo," 2017). Classic strain theory focuses
on the type of strain involving inability to achieve success or focused on a broader goal of the
middle–class status ("Strain theories – criminology – Oxford bibliographies – obo," 2017). Robert
Agnew developer of general strain theory in 1992, becoming the most leading version of strain
theory and major theories of crime ("Strain theories – criminology – Oxford bibliographies – obo,"
2017). General strain theory focuses on a wide range of strains including the inability to achieve a
variety of goals, loss of balanced passions ad negative treatment by others ("Strain theories –
criminology – Oxford bibliographies – obo," 2017). General strain theory deviates the explanation
of race, gender, ethnic, age, communities and society differences in crime rates ("Strain theories –
criminology – Oxford bibliographies – obo," 2017). People that gravitate to general strain theory are
more likely to relieve less support that increases the crime of rate. Differential association theory
was

Case Study On Popular Data Mining
Study on Popular Data Mining Algorithms/Techniques
Abstract: Data mining algorithms determine how the cases for a data mining model are analyzed.
Data mining model algorithms provide the decision–making capabilities needed to classify,
segment, associate and analyze data for the processing of data mining columns that provide
predictive, variance, or probability information about the case set. With an enormous amount of data
stored in databases and data warehouses, it is increasingly important to develop powerful tools for
analysis of such data and mining interesting knowledge from it. Data mining is a process of
inferring knowledge from such huge data. Data Mining has three major components Clustering or
Classification, Association Rules and Sequence Analysis.
Keywords: Data mining, Classification, Clustering, Algorithms
Introduction:
Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks.
In general, data mining tasks can be classified into two ... Show more content on Helpwriting.net ...
The selection and implementation of the appropriate data – mining technique is the main task in this
phase. This process is not straightforward; usually, in practice, the implementation is based on
several models, and selecting the best one is an additional task. By simple definition, in
classification/clustering we analyze a set of data and generate a set of grouping rules which can be
used to classify future data. One of the important problems in data mining is the Classification–rule
learning which involves finding rules that partition given data into predefined classes. An
association rule is a rule which implies certain association relationships among a set of objects in a
database. In sequential Analysis, we seek to discover patterns that occur in sequence. This deals with
data that appear in separate

Association Rule Mining Technique With Improve Fp _ Tree...
Association Rule Mining Technique with Improve FP_ Tree and Frequent Closed Patterns
Thesis submitted in partial fulfillment of the requirements for the award of degree of
Master of Science in COMPUTER SCIENCE
Under the supervision of:
Yuan Ling (袁凌)
Submitted By
Wasan Hassoon Itwee
(ID I201521052)
ABSTRACT
has resulted large amount of data stock in databases therefore the Data mining comes into model to
explore and analyses the databases to extract the interesting and previously obscure patterns and
rules well–known as association rule mining It was first introduced in 1993.
In DM Association rule mining becomes one ... Show more content on Helpwriting.net ...
There is an interesting alternative, recently proposed by Pasquier And others. Instead of mining the
complete set of FI sets and their associations, it needs only to find FCI(frequent closed item) sets
and their corresponding rules. An important inclusion is that mining FCI sets has the same power as
mining the full set of FI sets, but it will virtually reduce redundant rules to be generated and expand
both "efficiency and effectiveness of mining " By using CLOSET algorithm we can efficiently mine
the FCI sets.
KEYWORDS : Data mining ; Association rules ; FP–tree ; FCI ; CLOSET
. Introduction
– With the increment in IT (Information Technology) the size of the databases generated by the
organizations due to the availability of low–cost store and the development in the data pick
technologies is also increasing , Data mining (DM) also called KDD (Knowledge discovery in
databases) helps to identifying priceless information in such large databases. This valuable
information can help the decision producer to make exact futurity decisions, In Figure 1 describe
data mining techniques

Figure 1 : data mining techniques
–Why DM ?
Data mining has become a key technology

Analysis Of Mackintosh 's Approach Of Treating Cognitive...
Since radical behaviourists first described learning theory, it has undergone a significant change of
approach. In the paper "Has the Wheel Turned Full Circle? Fifty Years of Learning Theory, 1946–
1996", Mackintosh (1997) summarises what has been done in the field of learning theory since
1946. His main argument relates to the importance of the associative learning theory. He claims that
with appropriate modifications, it is the most powerful approach that science has.
In the presented review, I aim to critically discuss this paper. I start with criticising Mackintosh's
approach of treating cognitive and associative processes as two distinct. Then, I examine
Mackintosh's point of view on animal learning. I conclude that Machintosh's (1997) main claim
about the importance of the associative learning is supported by subsequent research. Nevertheless,
it is important not to neglect either associative, or cognitive approach, because they are not
fundamentally different, and, at least in humans, both of them play a significant role.
Cognitive versus associative approaches to learning
Mackintosh (1997) starts his paper with a critique of radical behaviourism. In the 1960s, Pavlovian
and Instrumental conditionings were main theories to explain animal and human behaviour.
However, with cognitivism coming into the picture, learning theory has dramatically improved.
Animal theorists started paying more attention to animal cognition, and nothing but well came from
it, as researchers

Professional Negligence Case
1. Review the hypothetical scenario at the beginning of this chapter. Has professional negligence
been committed? What defenses can be raised?
In the case of the attorney who is a recent law school graduate, he can be held negligent "if" he had
no training or experience in automobile accidents claims, because failure to have skills and learning
commonly possessed by members in good standing within a profession, constitutes professional
negligence. In addition, "if" the lawyer failed to deliver competent professional service that resulted
in the client's suffering damages, then professional negligence can be charged as well.
Like in this legal disciplinary proceeding: Toledo Bar Association v. Hales, 120 Ohio, St.3d 340
(Sup.Ct. 2008), where

Data Mining Chapter 11 Homework Essay examples
Department of Computer Science
Database and Data Mining, COS 514
Dr. Chi Shen
Homework No. 8, Chapter 13, Aklilu Shiketa
Q13. 3 Cosmetic Purchases
Consider the following Data on Cosmetics Purchases in Binary Matrix Form
a) Select several values in the matrix and explain their meaning.
Value
Cell
Meaning
0
For example, Row 1, Column2
At transaction #1 bag was not purchased. (shows absence of Bag in the transaction)
1
Row 10, column (2 and 3)
"If a Bag is purchased, a Blush is also purchased at that same transaction." ("If Bag, then Blush.")
While Bag is antecedent, Blush represents consequent.
1
Row 5, Column (3, 6, 8)
"If Blush and Concealer, then Bronzer. Item set {Blush, Concealer} = antecedent; { Bronzer} =
consequent
1
Row ... Show more content on Helpwriting.net ...
Support is the percentage or number of occurrences of items in both antecedent and consequent item
sets in a transaction.
In the case of the matrix : Support ( a) is the number or the percentage of the occurrence of {
Bronzer, Concealer, Brushes, Nail Polish}/ transaction
= Support (a) = {Bronzes, Concealer, Brushes, Nail Polish}/ transaction
Support (c) = It is the number of occurrence of the item set in the consequent.
= In this case it appears that Brushes, Concealers appeared in the consequent item 77 times.
Support (a U c) = This is the support of the combined item set. Therefore it will be the Union of

Support (a) = 103 and Support (c) =77, which is 62.
iii) For the first row explain the "Lift Ratio" and how it is calculated Lift Ratio is another way of
testing or judging the strength of an association rule. It helps to know the effectiveness of the rule in
finding the consequents. It is done by comparing the confidence of the rule with a benchmark
confidence value.
Benchmark confidence on the other hand is calculated in the following manner:
Benchmark confidence = no. transactions with consequent item set/ no. transactions in database.
Lift Ratio is the outcome of the comparison of Confidence to the Benchmark confidence.
It is the confidence of the rule divided by the confidence, assuming independence of consequent
from antecedent:
Lift

Data Analysis : Data Mining Essay
Data, Data everywhere. It is a precious thing that will last longer than the systems. In this
challenging world, there is a high demand to work efficiently without risk of losing any tiny
information which might be very important in future. Hence there is need to create large volumes of
data which needs to be stored and explored for future analysis. I am always fascinated to know how
this large amount of data is handled, stored in databases and manipulated to extract useful
information. A raw data is like an unpolished diamond, its value is known only after it is polished.
Similarly, the value of data is understood only after a proper meaning is brought out of it, this is
known as Data Mining.
Data mining is defined as the process of exploration and analysis of large data sets, and discovering
meaningful patterns and rules. The main objective of data mining is to design and work efficiently
with large data sets. Data mining helps resolving problems that are time consuming when traditional
techniques are used. Data mining techniques are used to predict future trends and to make wise
decisions. There are multiple Data Mining techniques available to the Data diggers to make their life
easy. In my study report I will be discussing about the different mining techniques, advantages and
disadvantages and also about a use case of the data mining techniques on shark attack dataset to
predict the attack of sharks based on various attributes.
Data Mining – An Overview
Explosion of

Was Closed Itemset Mining So Important Approaches And...
Association rule mining, One of the most important approaches and techniques that studied of data
mining. A lot of researchers' Inefficient the ideas to get the frequent item sets/ pattern sets. frequent
patterns and closed frequent patterns are two techniques the major objective it is to reducing the set
of extracted patterns to a smaller more interesting subset. Some frequent pattern mining often
generates a large number of frequent patterns, which imposes a big challenge on visualize,
comprehension and further analysis of the generated patterns. Mine a large amount useless frequent
patterns , it is requires more space and more time, and thus leads to high cost as well as
ineffectiveness. This require the need for finding small number ... Show more content on
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We will focus in our experiments on one of more efficient algorithm called CLOSET .
Keywords : Association rule mining , Frequent pattern, Closed pattern, CLOSET algorithm
1.1 Introduction
This master thesis addresses the data mining area known as closed itemset mining. The work
programme includes analysis a number of well–known algorithms from the literature, and then
modifying these algorithms in order to optimize their performance by reduce the number of frequent
pattern.
Data mining is the procedure of getting new patterns from large amount of data. Data mining is a
procedure of finding of beneficial information and patterns from huge data. It is also called as
knowledge discovery method, knowledge mining from data, knowledge extraction or data/ pattern
analysis. The main goal from data mining is to get patterns that were already unknown. The useful
of these patterns are found they can be used to make certain decisions for development of their
businesses. Data mining aims to discover implicit, already unknown, and potentially useful
information that is embedded in data.
Frequent itemsets play an main role in a lot of data mining tasks that try to get interesting patterns in
databases, such as association rules, clusters, sequences correlations, episodes and classier. Although
the number of all frequent

The Role Of Labelling Theory And Differential Association...
INTRODUCTION The focus of this paper will be on two contemporary criminological theories and
their application to the crime film, Eastern Promises. The two theories to be discussed, and
subsequently applied to the film, are labelling theory and differential association theory. Labelling
theory falls under the symbolic interactionist approach, and the primary level of analysis of this
theory is micro, as it tends to focus on the effect of labels on an individual's sense of "self". The
basis of labelling theory is that no act is inherently deviant; it is only when the act is labelled deviant
that it becomes so. When someone is labelled as deviant, they begin to see themselves as the label
they have been assigned. This can cause the behaviour to happen more frequently, as the individual
who has been labelled begins to see themselves as they label they have been given. A criticism of
labelling theory is that it lacks empirical validity, and is deterministic. There is no way to effectively
test this theory, so there is no way to know for sure how accurate the concept of labelling is and the
effect it has on an individual and their propensity towards criminality. This and other aspects of
labelling theory will be broken down and discussed later on in the paper. The second theory to be
discussed in this paper is Edwin Sutherland's theory of differential association. This theory is an
extension of social learning theory, and it follows the positivist approach. It also uses a micro

Algorithms, Algorithms And Consequences For Top-K...
4.3. Top–K Association Rule More than one algorithms & sequences were predicted for top–k
association administers mining. But, most of them do now not take advantage of the essential
definition of an association rule. As an occurrence, KORD discovers approaches with an unmarried
thing in the resulting, while the arrangement of principles of You et al. mines connection rules from
a move in lieu of an exchange database. To the wonderful of our concentration, least difficult best k
rules finds top–k affiliation rules predicated on a similar old meaning of an alliance run (with
various things, in an exchange database). the primary pivotal thought process that characterize this
calculation is that it characterizes the endeavor of mining the ... Show more content on
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With Ajax, web applications can send actualities to, and recuperate measurements from, a server
nonconcurrently (inside the legacy) without meddling with the disclosure and aura of the present site
page. Actualities might be recovered ingesting the XM HttpRequest protest. Regardless of the
category, the usage of XML isn't required and the solicitations do never again require being
nonconcurrently. Ajax isn't a solitary period, however a gathering of advancements. HTML and CSS
can be connected in collection to increase and style data. The DOM is gotten to with JavaScript to
powerfully show, and endorse the utilizer to have collaboration with, the measurements gave.
JavaScript and the XMLHttpRequest question give an approach to supplanting measurements
nonconcurrently among program and server to shun full page reloads and avert customer to converse
with the server that can keep server hits which may be ate up on the off chance that we don't utilize
AJAX. This is its miles one way to limit individual hits being hit to server and server load might be
diminished by methods for applying AJAX on shopper side. 4.6. R Language It's far a language that
becomes built for creating interactive graphs for data analysis, statistical modeling, simulation and
graphics. but, its general cause with a few sturdy techniques and functions that might be useful for
any

Association Rule Mining Model : Study Student Achievements
Association Rule Mining Model to Study Student Achievements Shyma Mogtaba Mohammed
Ahmed Faculty of Mathematical Sciences, University of Khartoum, Sudan
shymaamogtaba@gmail.com Abstract –Machine learning is a subdivision of Artificial Intelligence
(AI) that is concerned with the design and development of intelligent algorithms that enables
machines to learn from data without being programmed. Machine learning mainly focus on how to
automatically recognize complex patterns among data and make intelligent decisions. In this paper,
intelligent machine learning algorithms are used to classify the type of an eye disease based on
ophthalmology data collected from patients of Mecca hospital in Sudan. Three machine–learning
techniques are ... Show more content on Helpwriting.net ...
The rest of this paper is organized as follows: Section II reviews the literature done in the same
field. Section III explains the material and methods. Section IV presents and discusses the obtained
prediction model results. The paper conclusion and future work are presented on Section V. II.
RELATED WORK Many research have been conducted in the area of medical data classification
using machine learning the following paragraphs review and summarize these related works.
Decision tree analysis is used with a database of accommodative esotropic patients [2]. Decision
tree analysis of 354 accommodative esotropic patients resulted in the invention of two conjunctive
variables that predicted deterioration within the initial year of treatment much better than the fact
that was previously determined using standard statistical techniques. Treigys, P. and Šaltenis [8] was
investigated a disease classifier employing machine learning neural network approach. The neural
network with one hidden layer was adopted. The network activation function logsig as well as the
Levenberg–Marquardt learning algorithm applied, and the results

Data Mining and Knowledge Discovery
Rule induction is one of the major forms of data mining and is perhaps the most common form of
knowledge discovery in unsupervised learning systems. It is also perhaps the form of data mining
that most closely resembles the process that most people think about when they think about data
mining, namely "mining" for gold through a vast database. The gold in this case would be a rule that
is interesting – that tells you something about your database that you didn't already know and
probably weren't able to explicitly articulate (aside from saying "show me things that are
interesting"). The bane of rule induction systems is also its strength – that it retrieves all possible
interesting patterns in the database. This is strength in the sense that it leaves no stone unturned but
it can also be viewed as a weakness because the user can easily become overwhelmed with such a
large number of rules that it is difficult to look through all of them. You almost need a second pass
of data mining to go through the list of interesting rules that have been generated by the rule
induction system in the first place in order to find the most valuable gold nugget amongst them all.
This overabundance of patterns can also be problematic for the simple task of prediction because all
possible patterns are culled from the database there may be conflicting predictions made by equally
interesting rules. Automating the process of culling the most interesting rules and of combing the
recommendations

The Importance Of Knowledge Discovery, A Paramount Process...
Knowledge discovery is a paramount process in data mining wherein data can be analyzed from
divergent perspectives and summarized for future use. One of the most widely used data mining
process is association rule mining. Association mining rules are further classified by observing data
for patterns that are present and by consuming the criteria for analyzing the support and confidence
to identify the most closely relatedinterlinked processes. In association rule mining rules are
provoked and paramount relationships recognizedby analyzing data for pattern exploiting criteria
support and confidence. As users have withdrawn resources for determining the results in practice,
an algorithm that is being used to mine the top–k association rules ... Show more content on
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Moreover, performance of an algorithm also depends upon the size of the data sets.
2. Keywords
Association mining rule, Positive association mining rules, Negative association mining rules,
AJAX, Top–k rules, Sequential Pattern Mining.
3. Introduction
3.1. Background of the Study
Data mining has become astronomically paramount for most of the business domains among them
few of them are listed like marketing, financing and telecommunication. This has become possible
because of the development of data base technology and systems in recent past few years. Data
mining strategies is utilized for data processing. Operations performed on the data such as
accumulation, utilize or administration is called data processing. A few real life demonstrations that
can further demonstrate with the help of an example, a shop keeper requesting that client to fill in a
counter slip for data process and to maintain the record for future [5]. Affiliation rule mining is a
data mining strategy that can undoubtedly finds the patterns or association in astronomically
quantity of facts units. So as for statistics to be valuable, it should have the following characteristics:
Precise, Consummate, Malleable, Dependable, Pertinent, Simple, Timely Retrievable, and
Verifiable.
Association rule mining may be assumed as positive association rule mining. Effective affiliation
rule is verbalized as ''if individual purchase the item like spread and bread, at

The Code Of The Street Essay
In this paper, I plan to first describe the "Code of the Street" which is a term coined and a book
written by Elijah Anderson. I would also summarize and describe two journal articles that test
Anderson's idea of the "Code of the Street" for a more definite explanation. I will tell how the two
articles that I have chosen relates to some of the concepts that Anderson talked about in the book. I
will then define general strain theory and social learning or differential association theory. Lastly, I
will explain how general strain theory and social learning theory or differential association theory
explain some of the behaviors that were seen by the individuals in the book published by Anderson.
I will point out some of the individual's behavior and demonstrate whether it may lead to crime or
whether the behavior was learned in any way.
What does Elijah Anderson mean by the "Code of the Street". "... the street culture has evolved
a"code of the street," which amounts to a set of informal rules governing interpersonal public
behavior, particularly violence. The rules prescribe both proper comportment and the proper way to
respond if challenged. They regulate the use of violence and so supply a rationale allowing those
who are inclined to aggression to precipitate violent encounters in an approved way" ( Anderson,
1999 p. 33) According to Anderson, the "Code of the Street" is a set of rules bound to those who are
found to be at risk victims or those who are less privilege. Those

Racial Bias And Momendations In The Criminal Justice System
This overall inability to predict crime is due to many discrepancies found in the risk assessment.
First white defendants were predicted to be less risky than they actually were with white defendants
mislabeled low risk almost twice as often as black defendants. Black defendants were 45% more
likely to be assigned higher risk scores than white defendants when controlling for prior crimes,
future recidivism, age and gender. Black defendants were also twice as likely to be misclassified as
being higher risk for violent crimes than white defendants while white defendants that do reoffend
with violent crime were 63% more likely to have been misclassified as low risk for violent crime.
Finally, black defendants were 77% more likely to be ... Show more content on Helpwriting.net ...
Additionally, data from internet will not be a good way to determine whether a person is a terrorist
especially if that person has shown themselves critical of the Trump administration. This algorithm
may classify them as terrorist simply because they don't agree with current policies while clearly
disagreeing on policy does not many a person a terrorist. There are many approaches and algorithms
involved in machine learning. Some such algorithms are decision tree learning, association rule
learning, reinforcement learning and rule–based machine learning. All these algorithms sound
similar to what the names suggest in which decision trees are made to make decisions and learn,
association rule learning is from discovering relations between variables, reinforcement learning is
when it learns what to do through reinforcement, and rule– based learning is where rules are stored
about how to use knowledge. While these all can mirror to some extent variations of human learning
and decision making, not every aspect that occurs in a human brain is in the algorithms that exist
such as the higher–level processes that occur within decision making and learning so therefore it is
difficult to mirror all human aspects of learning and behavior in AI. Due to these differences in
learning, prejudice in AI is different than the usual mechanism seen in people. While AI is able to
learn stereotypes, and pick up on language bias like humans do through types of

Application Of A Smart Home System Essay
Now in smart home system and abroad there found a wide variety of household appliances. These
appliances are mostly used by the user and thus accumulating huge amount of life related data. This
data contains wide information about user behaviour. Collecting this data and mining it for user
behaviour analysis is now creating more interest in researchers. This paper approaches the improved
techniques for data mining and focuses on reduction of delay of user behaviour analysis using these
improved data mining techniques.
Keywords–Data Mining; Association Rule; User behaviour Analysis
I. INTRODUCTION
A smart home system has always under attention of global IT people and home appliances
manufacturers. A fully fledged smart home system will contain a wide variety of household
appliances. With the improvement in living standards of people, smart home is becoming the next
standard of home life. Smart home not only gives users a safe, healthy and comfortable living
environment, but also helps users to remotely monitor their home state with control home
appliances.[6] Many people start to pay attention to make full use of the data processing capabilities
of smart home devices to analyze data of the smart home appliances to extract the user 's
behavioural patterns of life and their habits, and finally provide users with personalized service and
remind.
Association rule is the mostly used data mining technique. As an important part of the data mining
technology, association rule

The Theory Of Association Rule Mining
has resulted large amount of data stock in databases therefore the Data mining comes into model to
explore and analyses the databases to extract the interesting and previously obscure patterns and
rules well–known as association rule mining It was first introduced in 1993.
In DM Association rule mining becomes one of the serious tasks of adjective technique which can
be defined as discovering important patterns from large collection of data. Mining frequent itemset
is very essential component of association rule mining.
Many Researchers developed techniques and a lot of algorithms for determining association rules.
The major problem ... Show more content on Helpwriting.net ...
This valuable information can help the decision producer to make exact futurity decisions, In Figure
1 describe data mining techniques
Data mining has become a key technology for companies and researchers in many fields , The
number and diversity of applications is growing over the years it is expected a significant increase in
this growth and there are many commercial space worked on DM prematurely recently been applied
DM in all areas in the banking, insurance, retail, telecommunications and pharmacy, health and
government and all e–business types and many of domain (Figure 2 ) Data mining applications in
2008(http://www. kdnuggets. com).,The authors highlight the importance of developing a
appropriate back testing environment that become the collection of Enough evidence to convince the
end users that the system can be used in practice
Figure 2: Data mining applications in
Why Association Rule Mining? it is the most effective data mining technique to discover hidden or
desired pattern among the large amount of data. It is responsible to find correlation relationships
among different data attributes in a large set of items in a database .
The information collected using Association Rule Discovery technique also helps the companies in

The Transpose Technique On Number Of Transactions Of...
The Transpose Technique to Reduce Number of Transactions of Apriori Algorithm
Narinder Kumar1, Anshu Sharma2, Sarabjit Kaur3
1 Research Scholar, Dept. Of Computer Science & Engineering, CT Institute of Technology &
Research, Jalandhar, Punjab 144008, India bhagatnanu1990@gmail.com 2 Assistant Professor,
Dept. Of Computer Science & Engineering, CT Institute of Technology & Research, Jalandhar,
Punjab 144008, India asharma106@yahoo.com 3Assistant Professor, Dept. Of Computer Science &
Engineering, CT Institute of Technology & Research, Jalandhar, Punjab 144008, India
er_sarabjitkaur35@rediffmail.com Abstract– Data mining is one of the essentially used and
interesting research areas. Mining association rule is one of the important research techniques in
data mining field. Many algorithms for mining association rules are proposed on the basis of Apriori
algorithm and improving the algorithm strategy but most of these algorithms not concentrate on the
structure of database. The proposed technique includes transposition of database with further
enhancement in this particular transposition technique. This approach will reduce the total scans
over the database and then time consumed to generate the association rules will be less.
Keywords– Association rules, Apriori , Transpose, Execution time and Iterations.
I. INTRODUCTION
Data Mining is known as the process of analyzing data to extract interesting patterns and
knowledge. Data

Malware Analysis And Detection Techniques
MALWARE ANALYSIS/DETECTION TECHNIQUES
Sikorski & Honig (2012), explain the fact that when carrying out malware analysis and detection,
only the malware executable is present, which is usually not in natural language form. A variety of
tools and techniques need to be employed to ensure that the underlying information is revealed. Two
basic approaches to malware analysis and detection include: static analysis (observing the malware
without running it), and dynamic analysis (running the malware). They can be done either in the
basic form or more advanced ways.
Static Analysis
In the basic form, static analysis involves carefully observing the executable file without looking at
the actual commands or instructions. This is done to ascertain that a file is indeed malicious, give
information about its functions, and occasionally give information that will enable one produce
simple network signatures. This process is straightforward and can be performed quickly, but in
most cases, it is not effective when dealing with sophisticated malware, and may miss significant
behaviours. An example of static analysis is the use of antivirus software such as AVG for malware
analysis. Unique identifiers called hashes can also be used to identify malware in static analysis.
Dynamic Analysis
In the basic form, dynamic analysis techniques involve both running the malware code and
examining its behaviour on the system or network so as to remove the infection, derive effective
signatures, or

Sociological Theories For Deviance Fall Under The Concept...
According to sociologist Edwin Lemert, the majority of sociological theories for deviance fall under
the concept of primary deviance. However, Lemert considered secondary deviance to be more
important. Everyone is guilty of primary deviance. However, this does not mean that they perceive
themselves to be a bad person. This is perception is where secondary deviance comes in. With
secondary deviance, the individual, is labeled by the act of deviance that they committed, whether it
is by themselves or by others. Eventually, this label is accepted by the individual, as a part of their
identity. While primary deviance consists of an act that takes up a small amount of time in a person's
existence, secondary deviance is something that sticks with the individual for the long term.
Potentially, this type of deviance can stick with an individual for the rest of their lives. There are two
different theories that can compare and contrast how a concept such as secondary deviance can gain
a foothold in an individual's life. These two theories are the differential association theory and the
control theory.
The differential association theory touts deviance as something that is learned. It is learned through
the inter–personal relationships and influences that an individual has with those close to them.
Furthermore, the theory declares that learning deviance is similar to, if not identical to, learning
anything else. With the differential association theory, the individual is taking a

How Can A User Buyer System Be Used For Enhance Personal...
Research Question: How can a user buyer system be used to enhance personal purchasing decisions?
Sub–question: Can data mining be used to analyze consumer behaviors and preferences in order to
satisfy purchasing decision?
Literature Review
Presently, the online shopping has become increasing in today's market. Many consumers turn to use
the internet for shopping, as a result the online recommender systems have realized for facilitating
consumer needs and their interests based on previous shopping behaviors. According to Jiang,
Shang, and Liu, Y. (2010) the recommender system is defined as a decision tool that designs to assist
consumers need and provide products information that match consumer' interests based on
analyzing previous ... Show more content on Helpwriting.net ...
However, these techniques lead recommender systems face with the important problems such as
sparsity, precision, and scalability problem. Thus, applying data mining techniques to the
recommender systems is concerned as a solution for solve this problem (Deuk et al., 2011). Its
capability could play a significant role for analyzing and predicting valuable customer knowledge,
for instance, purchase behaviors, customer preferences, and interests. Also, then using that
knowledge for suggesting products/services that suit and satisfy customers (Kumar Guptaa and
Guptab, 2010).
2. Data mining There are various techniques involve in the recommender systems. The popular one
that is concerned to solve the existing problems is data mining techniques. Many recommender
systems implement data mining technique as a method to analyze and discover significant consumer
knowledge. Mohammed et al. (2013) and Kumar Guptaa and Guptab (2010) state that data mining is
defined as a systematical process of exploring and discovering valuable knowledge from large
volume of data in data repositories. Data mining aims to extract and disclose hidden vital patterns
from data. It has potential to collect relevant data in large volume of database and data warehouse.
Additionally, the data mining can use to anticipate future customer trends and help business to gain
competitiveness (Liao, Chen, and Lin, 2011).
2.1 Data mining techniques
Deuk et al. (2011) said that the recommender

Support Alone Essay
The usefulness of just using support alone will only tell you how many times each item appear
together based on the transactions in the bakery. The support values are smaller than I expected. The
highest support is 0.053 (Apricot Danish & Cherry Tart). So the highest frequency an item in the
bakery appears together is only 5.3%. This might be the case because depending on a customer's
"sweet tooth" the purchase may be different or the bakery has a different special going each day. I
do not believe we can judge itemsets just on support alone, again because it is only base on when
items appear together. For transaction number 113, it supports if you buy, Coffee Éclair, Hot Coffee,
and Almond Twist, it would have a 99.8% confidence that you will also buy an Apple Pie. When we
look at the lift, we see that we are 13.462 times more likely (confident) that we ... Show more
content on Helpwriting.net ...
The reason why I would choose these is based on the confidence and lift. These rules seem like they
would be actionable based on each of these items appear together 2% of the time in support data.
The bakery can use these rules to help make a decision on what is needed or stock in the store. It can
also have Number 114 – the support is 2% that (Raspberry Cookie, Raspberry Lemonade, Lemon
Lemonade, Green Tea) will appear together with (Lemon Lemonade), with a 100% confidence that
if the premises being purchased the conclusion will also be purchased. To strengthen this to most
likely happen is the lift. This rule states 15.026 times more likely for this to happen. Number 115 –
the support is 2% that (Raspberry Cookie, Lemon Cookie, Lemon Lemonade, Green Tea) will
appear together with (Raspberry Lemonade), with a 100% confidence that if the premises being
purchased the conclusion will also be purchased. To strengthen this to most likely happen is the lift.
This rule states 14.609 times more likely for this to

A Research Study On Data Mining
Data mining is the process of discovering patterns, trends, correlations from large amounts of data
stored electronically in repositories, using statistical methods, mathematical formulas, and pattern
recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives
and discover useful trends, patterns and associations. As discussed in the previous chapter, the
healthcare organizations are producing massive amounts of electronic medical records, which are
impossible to process using traditional technologies (e.g., Microsoft excel). Therefore data mining is
becoming very popular in this field as it can be used to identify the presence of chronic disease,
detect the cause of the disease, analyze the effectiveness of treatment methods, predict different
medical events, identify the side effects of the drugs, and so on. Kidney diseases such as CKD or
AKI require immediate detection and medical attention based on the patient's clinical condition,
medical history, medication history and some demographical factors. From the literature survey, we
discovered a good number of studies and tools that used data mining methods such as clustering,
association, and classification to improve the decision–making ability of the healthcare providers
regarding kidney disease. In the subsequence sections in this chapter, we present an overview of the
data mining methods and discuss how they have been used in existing literature. 4.1. Overview of
data mining

Network Security Consists Of The Procurements And Strategies
ARTIFICIAL INTELLIGENCE RESEARCH PAPER
ANUSHA SHER (A03771526)
ABSTRACT
Network security comprises of the procurements and strategies embraced by a network executive to
anticipate and screen unapproved access, abuse, change, or disavowal of a computer network and
network–open assets. Network security includes the approval of access to information in a network,
which is controlled by the network chairman. As the computer network is increasing drastically, so
are the threats to the computer network also. With the quick improvement of computer network, the
network is defying a developing number of dangers. Hence, it is exceptionally paramount to
evaluate the dangers for the network data framework. This paper draws information mining
innovation focused around affiliation principles into the field of danger evaluation, exhibiting a
network security hazard evaluation model focused around affiliation standards. The model mines
information from history defenselessness database to run across affiliation tenets of vulnerabilities,
and it is connected on danger evaluation of vulnerability.
INTRODUCTION
Data mining, an interdisciplinary subfield of computer science, is the computational methodology of
finding examples in substantial data sets including routines at the crossing point of manmade
brainpower, machine learning,

Hacking And The Social Learning Theory
favorable or consequential outcomes that are observed. Imitation is more likely to be responsible for
the onset of the behavior rather than a continuation of the behavior (Morris & Higgins, 2010).
Definitions refer to the influence of factors with one's having fdefine the appriateness f an act. This
involves one's rationalization, neutralizations, justifications, and excuses toward engaging in a
certain behavior. The more someone has learned and approves of definitions favorable to deviance,
the more likely they will participate in that behavior (Morris & Higgins, 2010). Hacking and the
Social Learning Theory The social learning theory views hackers as individuals who are socialized
into breaking rules through peer–association. A majority of hackers are young and learn from their
friends in a type of communal environment. Studies show that this behavior takes place in a
distinctive socio–cultural context and "communal" structure. Just like social learning takes place in
the terrestrial world, it also takes place in the virtual world. Peer association and social learning in
the hacking subculture takes place in both the terrestrial and virtual worlds. In the "terrestrial"
world, hackers participate in organized conferenes, such as the annual DEFCON hacker gathering,
at which knowledge, tools and tales are exchanged. Chapters of the 2600 hackers organization meet
weekly in towns and cities across the US. In "virtual" or online settings, peer groups are formed and

Improving Navigation Pattern By Association Rule Essay
IMPROVE NAVIGATION PATTERN BY ASSOCIATION RULE
NAVIGATION
 Abstract
Association rule mining can be used to extract patterns of a website visitors' behavior. This data can
be used to improve web marketing (e business)techniques or to improve the web surfing experience.
Here we are applying association rule on web usage log file of an institution. We are using
association rule as a interesting measures and verifying their values in two different period of time.
We will see how this comparison brings extra important information about association rules
generation and helps a webmaster make more and more accurate decisions about the website
development and enhancements. Keywords–: Web usage mining, association rules, interestingness
measures, E – business
 Introduction
With the fast development of e–commerce (electronic commerce) sector in last few years, the
importance and applicability of intelligent data management techniques has become essential in the
e–business sector. The data about client behavior can play important role in maximizimg on–line
application effectiveness,this will rise client satisfaction, and competitiveness.
Association rule mining was invented to extract patterns from transactional databases. As stated, an
association rule is an method applied in the form X →Y, where X and Y are sets of items.
Association rule mining finds all such conditions which

Survey On Distributed Data Mining
Survey on Distributed data mining
Dr.S.kannan, Vairaprakash Gurusamy,
Associate Professor, Research Scholar,
Department of Computer Applications, Department of Computer Applications,
Madurai Kamaraj University, Madurai Kamaraj University, skannanmku@gmail.com
vairaprakashmca@gmail.com
Abstract
Traditionally Data Mining is a process of extracting useful knowledge from a large volume of data
set. The generated knowledge is applied and used for the most of the applications in all the areas
such as science, engineering, business, research, social, health, education, entertainment and all. As
all the ... Show more content on Helpwriting.net ...
Introduction
The continuous developments in information and communication technology have recently led to
the appearance of distributed computing environments, which comprise several, and different
sources of large volumes of data and several computing units. The most prominent example of a
distributed environment is the Internet, where increasingly more databases and data streams appear
that deal with several areas, such as meteorology, oceanography, economy and others. The
application of the classical knowledge discovery process in distributed environments requires the
collection of distributed data in a data warehouse for central processing. However, this is usually
either ineffective or infeasible for the following reasons:
Storage Cost: It is obvious that the requirements of a central storage system are enormous. A
classical example concerns data from the astronomy science, and especially images from earth and
space telescopes. The size of such databases is reaching the scales of exabytes (1018 bytes) and is
increasing at a high pace. The central storage of the data of all telescopes of the planet would require
a huge data warehouse of enormous cost.
Communication Cost: The transfer of huge data volumes over network might take extremely much
time and also require an unbearable financial cost. Even a small volume of data might create
problems in wireless network environments with limited bandwidth. Note also that

Street Code : The Cultural Argument Essay
Street Code: The Cultural Argument
The Elijah Anderson's article, "Code of the Streets" is a perfect illustration for cultural arguments
because it involves environments that are susceptible to learning a criminal culture; even up to a
point of promoting that type of criminal behavior as "normal". It also has links to Differential
Association and Social Learning theories of crime
Culture transmission theories or cultural theories are theories that try to explain why, at a macro–
level, some environments are breeding grounds for criminal behavior. Also why these types of
environments develop a culture that its participants consider this type of criminal behavior is
acceptable and necessary (Feldmeyer, Cultural Transmission Theories, 2015).
And on the other hand how "Code of the Streets" shows links to the Differential Association and
Social Learning theories of crime. The Differential Association (closely related to Social
Disorganization theory), developed by Edwin Sutherland, and Social Learning theory, developed by
Ronald Akers, both theories of crime are theories that try to explain, at a micro–level, why
individuals rather than groups of individuals commit crime (Feldmeyer, Differential Association and
Social Learning, 2015).
Sutherland's theory piggyback on Social Disorganization theory by answering some of the critic's
questions about why only some people in crime–prone neighbors commit crime while many others
do not. While Aker's theory pick up where Sutherland

Understanding Inclusive Learning and Teaching in Lifelong...
City and Guilds 6302 Award in Preparing to Teach in the Lifelong Learning Sector Unit 002
Understanding Inclusive Learning and Teaching in Lifelong Learning Assessment Task 1 By Robert
A J Gue Submission Date 3 May 2012 I have been asked by the Scout Association to give guidance
on how to incorporate inclusive learning an teaching in to their training scheme to meet the needs of
the leaders who come from diverse backgrounds. The Scout Association is part of a worldwide
educational youth movement. The values, which underpin and inspire its work are embodied in the
Scout Promise and Law and in the Purpose of the Association. Within this framework, the
Association is committed to equality of ... Show more content on Helpwriting.net ...
Those who prefer a visual learning style.. ▪ look at the teacher's face intently ▪ like looking at wall
displays, books etc. ▪ often recognize words by sight ▪ use lists to organize their thoughts ▪ recall
information by remembering how it was set out on a page Those who prefer an auditory learning
style... ▪ like the teacher to provide verbal instructions ▪ like dialogues, discussions and plays ▪ solve
problems by talking about them ▪ use rhythm and sound as memory aids Those who prefer a
kinaesthetic learning style... ▪ learn best when they are involved or active ▪ find it difficult to sit still
for long periods ▪ use movement as a memory aid Those who prefer a tactile way of learning... ▪ use
writing and drawing as memory aids ▪ learn well in hands–on activities like projects and
demonstrations Source: Dr's Bandler, R. and Grinder, J. in the Field of Neuro–Linguistic
Programming To summarise we have established that scout leaders come from many different
cultures and religions and some have a number of physical

National Reading Panel Summary
The National Reading Panel (NRP) and the International Reading Association (IRA) are two
different associations that provide research findings on the best practices for teaching and learning
literacy. Reading and understanding the findings of both associations is important for educators and
parents to boost the literacy rates of our children. Below I will summarize the reading standards that
our outlined by both and provide my thoughts and insights.
The NRP concludes that children need to be taught certain instructional methods to excel in reading.
The methods include phonemic awareness, phonics skills, reading with fluency, and comprehension
strategies including vocabulary development. The panel concludes that phonemic awareness should
begin in preschool and continue through the elementary school years, phonics instruction should
begin in kindergarten through sixth grade and once children are reading, fluency and comprehension
skills need to be taught through various techniques throughout the elementary years and beyond.
The panel goes on to explain detailed methods to teach the different standards. Beginning with
phonemic awareness, early in education, the findings conclude that children who have direct
instruction improved their reading skills more that children who did not receive this instruction. The
understanding that oral language is made up of connected phonemes gives children the foundation to
read and write. Once a child has phonemic understanding, the

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