This document presents a hybrid algorithm that combines Apriori Growth and FP-Split Tree algorithms for web usage mining. The algorithm has two phases: 1) It constructs an FP-Split Tree from web logs in a single pass, reducing complexity compared to FP-Tree which requires two passes. 2) It mines frequent patterns from the FP-Split Tree using an Apriori Growth approach instead of FP-Growth to avoid repeatedly recreating trees. The algorithm was tested on university website logs and showed better performance than traditional FP-Tree and Apriori methods, as it was faster at extracting frequent patterns for different support counts.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Existing parallel digging calculations for visit itemsets do not have a component that empowers programmed parallelization, stack adjusting, information conveyance, and adaptation to non-critical failure on substantial bunches. As an answer for this issue, we outline a parallel incessant itemsets mining calculation called FiDoop utilizing the MapReduce programming model. To accomplish compacted capacity and abstain from building contingent example bases, FiDoop joins the incessant things Ultrametric tree, as opposed to ordinary FP trees. In FiDoop, three MapReduce occupations are actualized to finish the mining undertaking. In the essential third MapReduce work, the mappers autonomously disintegrate itemsets, the reducers perform mix activities by building little Ultrametric trees, and the genuine mining of these trees independently. We actualize FiDoop on our in-house Hadoop group. We demonstrate that FiDoop on the group is touchy to information dissemination and measurements, in light of the fact that itemsets with various lengths have diverse decay and development costs. To enhance FiDoop's execution, we build up a workload adjust metric to quantify stack adjust over the group's registering hubs. We create FiDoop-HD, an augmentation of FiDoop, to accelerate the digging execution for high-dimensional information investigation. Broad tests utilizing genuine heavenly phantom information exhibit that our proposed arrangement is productive and versatile.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Existing parallel digging calculations for visit itemsets do not have a component that empowers programmed parallelization, stack adjusting, information conveyance, and adaptation to non-critical failure on substantial bunches. As an answer for this issue, we outline a parallel incessant itemsets mining calculation called FiDoop utilizing the MapReduce programming model. To accomplish compacted capacity and abstain from building contingent example bases, FiDoop joins the incessant things Ultrametric tree, as opposed to ordinary FP trees. In FiDoop, three MapReduce occupations are actualized to finish the mining undertaking. In the essential third MapReduce work, the mappers autonomously disintegrate itemsets, the reducers perform mix activities by building little Ultrametric trees, and the genuine mining of these trees independently. We actualize FiDoop on our in-house Hadoop group. We demonstrate that FiDoop on the group is touchy to information dissemination and measurements, in light of the fact that itemsets with various lengths have diverse decay and development costs. To enhance FiDoop's execution, we build up a workload adjust metric to quantify stack adjust over the group's registering hubs. We create FiDoop-HD, an augmentation of FiDoop, to accelerate the digging execution for high-dimensional information investigation. Broad tests utilizing genuine heavenly phantom information exhibit that our proposed arrangement is productive and versatile.
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
In the development, standardization and implementation of LTE Networks based on Orthogonal Freq. Division Multiple Access (OFDMA), simulations are necessary to test as well as optimize algorithms and procedures before real time establishment. This can be done by both Physical Layer (Link-Level) and Network (System-Level) context. This paper proposes Network Simulator 3 (NS-3) which is capable of evaluating the performance of the Downlink Shared Channel of LTE networks and comparing it with available MatLab based LTE System Level Simulator performance.
FP growth algorithm represents the database in the form of a tree called a frequent pattern tree or FP tree. This tree structure will maintain the association between the itemsets.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Apriori algorithm is one of the best algorithm in Data Mining field that used to find frequent item-sets. The apriori property tells us that all non-empty subsets of a frequent itemset must also be frequent.
This algorithm is proposed by R. Agrawal and R. Srikant
Frequent Pattern growth algorithm provides better performance than Apriori algorithm. This approach used to detect frequent itemsets in database. It has two phase. In first phase, it constructs a suffix tree and in next, it starts mining recursively.
The recursion process is shown in details in presentation with figure.
Frequent pattern mining techniques helpful to find interesting trends or patterns in
massive data. Prior domain knowledge leads to decide appropriate minimum support threshold. This
review article show different frequent pattern mining techniques based on apriori or FP-tree or user
define techniques under different computing environments like parallel, distributed or available data
mining tools, those helpful to determine interesting frequent patterns/itemsets with or without prior
domain knowledge. Proposed review article helps to develop efficient and scalable frequent pattern
mining techniques.
Skip List: Implementation, Optimization and Web SearchCSCJournals
Even as computer processing speeds have become faster and the size of memory has also increased over the years, the need for elegant algorithms (programs that accomplish such tasks/operations as information retrieval, and manipulation as efficiently as possible) remain as important now as it did in the past. It is even more so as more complex problems come to the fore. Skip List is a probabilistic data structure with algorithms to efficiently accomplish such operations as search, insert and delete. In this paper, we present the results of implementing the Skip List data structure. The paper also addresses current Web search strategies and algorithms and how the application of Skip List implementation techniques and extensions can bring about optimal search query results.
Weighted frequent pattern mining is suggested to find out more important frequent pattern by considering different weights of each item. Weighted Frequent Patterns are generated in weight ascending and frequency descending order by using prefix tree structure. These generated weighted frequent patterns are applied to maximal frequent item set mining algorithm. Maximal frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we proposed an efficient algorithm to mine maximal weighted frequent pattern mining over data streams. A new efficient data structure i.e. prefix tree and conditional tree structure is used to dynamically maintain the information of transactions. Here, three information mining strategies (i.e. Incremental, Interactive and Maximal) are presented. The detail of the algorithms is also discussed. Our study has submitted an application to the Electronic shop Market Basket Analysis. Experimental studies are performed to evaluate the good effectiveness of our algorithm..
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
In the development, standardization and implementation of LTE Networks based on Orthogonal Freq. Division Multiple Access (OFDMA), simulations are necessary to test as well as optimize algorithms and procedures before real time establishment. This can be done by both Physical Layer (Link-Level) and Network (System-Level) context. This paper proposes Network Simulator 3 (NS-3) which is capable of evaluating the performance of the Downlink Shared Channel of LTE networks and comparing it with available MatLab based LTE System Level Simulator performance.
FP growth algorithm represents the database in the form of a tree called a frequent pattern tree or FP tree. This tree structure will maintain the association between the itemsets.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Apriori algorithm is one of the best algorithm in Data Mining field that used to find frequent item-sets. The apriori property tells us that all non-empty subsets of a frequent itemset must also be frequent.
This algorithm is proposed by R. Agrawal and R. Srikant
Frequent Pattern growth algorithm provides better performance than Apriori algorithm. This approach used to detect frequent itemsets in database. It has two phase. In first phase, it constructs a suffix tree and in next, it starts mining recursively.
The recursion process is shown in details in presentation with figure.
Frequent pattern mining techniques helpful to find interesting trends or patterns in
massive data. Prior domain knowledge leads to decide appropriate minimum support threshold. This
review article show different frequent pattern mining techniques based on apriori or FP-tree or user
define techniques under different computing environments like parallel, distributed or available data
mining tools, those helpful to determine interesting frequent patterns/itemsets with or without prior
domain knowledge. Proposed review article helps to develop efficient and scalable frequent pattern
mining techniques.
Skip List: Implementation, Optimization and Web SearchCSCJournals
Even as computer processing speeds have become faster and the size of memory has also increased over the years, the need for elegant algorithms (programs that accomplish such tasks/operations as information retrieval, and manipulation as efficiently as possible) remain as important now as it did in the past. It is even more so as more complex problems come to the fore. Skip List is a probabilistic data structure with algorithms to efficiently accomplish such operations as search, insert and delete. In this paper, we present the results of implementing the Skip List data structure. The paper also addresses current Web search strategies and algorithms and how the application of Skip List implementation techniques and extensions can bring about optimal search query results.
Weighted frequent pattern mining is suggested to find out more important frequent pattern by considering different weights of each item. Weighted Frequent Patterns are generated in weight ascending and frequency descending order by using prefix tree structure. These generated weighted frequent patterns are applied to maximal frequent item set mining algorithm. Maximal frequent pattern mining can reduces the number of frequent patterns and keep sufficient result information. In this paper, we proposed an efficient algorithm to mine maximal weighted frequent pattern mining over data streams. A new efficient data structure i.e. prefix tree and conditional tree structure is used to dynamically maintain the information of transactions. Here, three information mining strategies (i.e. Incremental, Interactive and Maximal) are presented. The detail of the algorithms is also discussed. Our study has submitted an application to the Electronic shop Market Basket Analysis. Experimental studies are performed to evaluate the good effectiveness of our algorithm..
Prediction of Fault in Distribution Transformer using Adaptive Neural-Fuzzy I...ijsrd.com
In this paper, we present a new method for simultaneous diagnosis of fault in distribution transformer. It uses an adaptive neuro-fuzzy inference system (ANFIS), based on Dissolved Gas Analysis (DGA). The ANFIS is first “trained†in accordance with IEC 599, so that it acquires some fault determination ability. The CO2/CO ratios are then considered additional input data, enabling simultaneous diagnosis of the type and location of the fault. Diagnosis techniques based on the Dissolved Gas Analysis (DGA) have been developed to detect incipient faults in distribution transformers. The quantity of the dissolved gas depends fundamentally on the types of faults occurring within distribution transformers. By considering these characteristics, Dissolved Gas Analysis (DGA) methods make it possible to detect the abnormality of the transformers. This can be done by comparing the Dissolved Gas Analysis (DGA) of the transformer under surveillance with the standard one. This idea provides the use of adaptive neural fuzzy technique in order to better predict oil conditions of a transformer. The proposed method can forecast the possible faults which can be occurred in the transformer. This idea can be used for maintenance purpose in the technology where distributed transformer plays a significant role such as when the energy is to be distributed in a large region.
Intelligent Fault Identification System for Transmission Lines Using Artifici...IOSR Journals
Transmission and distribution lines are vital links between generating units and consumers. They are
exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be
immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults
on electric power transmission lines using artificial neural networks. A feed forward neural network is
employed, which is trained with back propagation algorithm. Analysis on neural networks with varying number
of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks
in each step. The developed neural network is capable of detecting single line to ground and double line to
ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial
neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory
performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system.
Hardware implementation of neural network is done on TMS320C6713.
Result analysis of mining fast frequent itemset using compacted dataijistjournal
Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena,
making easy to industrial decision support systems and continues to expand even beyond imagination in
one such promising field like Artificial Intelligence and facing the real world challenges. Association rules
forms an important paradigm in the field of data mining for various databases like transactional database,
time-series database, spatial, object-oriented databases etc. The burgeoning amount of data in multiple
heterogeneous sources coalesces with the impediment in building and preserving central vital repositories
compels the need for effectual distributive mining techniques.
The majority of the previous studies rely on an Apriori-like candidate set generation-and-test approach.
For these applications, these forms of aged techniques are found to be quite expensive, sluggish and highly
subjective in case there exists long length patterns.
Result Analysis of Mining Fast Frequent Itemset Using Compacted Dataijistjournal
Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena, making easy to industrial decision support systems and continues to expand even beyond imagination in one such promising field like Artificial Intelligence and facing the real world challenges. Association rules forms an important paradigm in the field of data mining for various databases like transactional database, time-series database, spatial, object-oriented databases etc. The burgeoning amount of data in multiple heterogeneous sources coalesces with the impediment in building and preserving central vital repositories compels the need for effectual distributive mining techniques.
The majority of the previous studies rely on an Apriori-like candidate set generation-and-test approach. For these applications, these forms of aged techniques are found to be quite expensive, sluggish and highly subjective in case there exists long length patterns.
Data Mining plays an important role in extracting patterns and other information from data. The Apriori Algorithm has been the most popular techniques infinding frequent patterns. However, Apriori Algorithm scans the database many times leading to large I/O. This paper is proposed to overcome the limitaions of Apriori Algorithm while improving the overall speed of execution for all variations in ‘minimum support’. It is aimed to reduce the number of scans required to find frequent patters.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
In today’s world there is a wide availability of huge amount of data and thus there is a need for turning this
data into useful information which is referred to as knowledge. This demand for knowledge discovery
process has led to the development of many algorithms used to determine the association rules. One of the
major problems faced by these algorithms is generation of candidate sets. The FP-Tree algorithm is one of
the most preferred algorithms for association rule mining because it gives association rules without
generating candidate sets. But in the process of doing so, it generates many CP-trees which decreases its
efficiency. In this research paper, an improvised FP-tree algorithm with a modified header table, along
with a spare table and the MFI algorithm for association rule mining is proposed. This algorithm generates
frequent item sets without using candidate sets and CP-trees.
Generation of Potential High Utility Itemsets from Transactional DatabasesAM Publications
Mining high utility item sets from a transactional database refers to the discovery of item sets with high utility.
Previous algorithm such as Apriori and Fp-Growth incurs the problem of producing a large number of candidate item sets for
high utility item sets. Such large number of candidate item sets degrades the mining performance in terms of execution time. So,
to improve the mining performance Up-Growth came into existence. Up-Growth effectively mines the potential high utility item
sets from the Transactional database. The information of high utility item sets is maintained in a tree-based data structure named
utility pattern tree (UP-Tree) such that candidate item sets can be generated efficiently with only two scans of database. The
performance of UP-Growth is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets.
A Study of Various Projected Data Based Pattern Mining Algorithmsijsrd.com
The time required for generating frequent patterns plays an important role. Some algorithms are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating frequent pattern from the various algorithms. We have explored the unifying feature among the internal working of various mining algorithms. The work yields a detailed analysis of the algorithms to elucidate the performance with standard dataset like Mushroom etc. The comparative study of algorithms includes aspects like different support values, size of transactions.
In this paper, we present a literature survey of existing frequent item set mining algorithms. The concept of frequent item set mining is also discussed in brief. The working procedure of some modern frequent item set mining techniques is given. Also the merits and demerits of each method are described. It is found that the frequent item set mining is still a burning research topic.
In the recent years the scope of data mining has evolved into an active area of research because of the previously unknown and interesting knowledge from very large database collection. The data mining is applied on a variety of applications in multiple domains like in business, IT and many more sectors. In Data Mining the major problem which receives great attention by the community is the classification of the data. The classification of data should be such that it could be they can be easily verified and should be easily interpreted by the humans. In this paper we would be studying various data mining techniques so that we can find few combinations for enhancing the hybrid technique which would be having multiple techniques involved so enhance the usability of the application. We would be studying CHARM Algorithm, CM-SPAM Algorithm, Apriori Algorithm, MOPNAR Algorithm and the Top K Rules.
An improvised tree algorithm for association rule mining using transaction re...Editor IJCATR
Association rule mining technique plays an important role in data mining research where the aim is to find interesting
correlations between sets of items in databases. The apriori algorithm has been the most popular techniques in finding frequent
patterns. However, when applying this method a database has to be scanned many times to calculate the counts of the huge umber
of candidate items sets. A new algorithm has been proposed as a solution to this problem. The proposed algorithm is mainly
concentrated to reduce the candidate sets generation and also aimed to increase the time of execution of the process
Association rules are the main techniques to
determine the frequent item set in data mining. Apriori
algorithm is the classic algorithm of association rules, which
enumerate all of the frequent item sets. If database is large, it
takes too much time to scan the database. The improved
algorithm is verified, the results show that the improved
algorithm is reasonable and effective, and can extract more
valuable information.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
PHP Frameworks: I want to break free (IPC Berlin 2024)
G017633943
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 6, Ver. III (Nov – Dec. 2015), PP 39-43
www.iosrjournals.org
DOI: 10.9790/0661-17633943 www.iosrjournals.org 39 | Page
A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For
Web Usage Mining
Dr. Parvinder Singh1
, Vijay Dahiya2
1
(Department of Computer Science, DCRUST, MURTHAL, India)
2
(Department of Computer Science, DCRUST, MURTHAL, India)
Abstract : Internet is the most active and happening part of everyone’s life today. Almost every business or
service or organization has its website and performance of the site is an important issue. Web usage mining
based on web logs is an important methodology for optimizing website’s performance over the internet.
Different mining techniques like Apriori method, FP Tree methodology, K-Means method etc. have been
proposed by different researchers in order to make the data mining more effective and efficient. Many people
have modeled Apriori or FP Tree in their own way to increase data mining productiveness. Wu proposed
Apriori Growth as a hybrid of Apriori and FP Tree algorithm and improved FP Tree by mining using Apriori
and removed the complexity involved in FP Growth mining. Lee proposed FP Split Tree as a variant of FP Tree
and reduced the complexity by scanning the database only once against twice in FP Tree method. This research
proposes a new hybrid algorithm of FP Split and Apriori growth which combines the positives of both the
algorithms to create a new technique which provides with a better performance over the traditional methods.
The new proposed algorithm was implemented in java language on web logs obtained from IIS server and the
computational results of the proposed method performs better than traditional FP Tree method, Apriori
Method.
Keywords: Apriori Growth, FP Split, Frequent Patterns
I. Introduction
Now days, the most dynamic place in world on the earth before land and sea is internet. Internet is
having a huge, volatile, varied, heterogeneous, semi-structured, and ever progressing data. The whole
businesses, government schemes, services which are provided over the internet are mainly dependent on
efficiency of the data mining techniques. The applied data mining techniques can provide for an increased
profit, competitiveness among the I.T firms which is a prerequisite in this 21st
century of science and
technology.
II. Related work
Data mining which is still in the arena of discovery is being the hot topic of researchers in the past.
Various algorithms have been proposed with numerous modifications in the field of data mining. Some of the
notable works are as follows:-
A. APRIORI Algorithm: Apriori algorithm which is one of the widely and the oldest used algorithm in data
mining is based on the algorithm of breadth first search algorithm where it makes use of a data tree structure in
order to count candidate item sets in an efficient manner based on the user desired support count. The algorithm
generates the candidate item sets of length x from item sets of length x-1. Once, after the generation of candidate
item sets it prunes those candidates which do not satisfy minimum support count and which have an
inappropriate sub pattern.
Limitations:
Though Apriori algorithm is one of the simplest algorithm, still it suffers with certain limitations.
1. It is very expensive to grasp a huge number of candidate sets. The amount of candidates to be
generated increases exponentially with increasing n-item set.
2. It is very difficult and a tiresome task for scanning the database repeatedly and looking for a greater
number of candidates by matching their pattern, which is very necessary for mining large patterns.
B. FP Growth Algorithm: In order to get the mostly used item sets without any generation of candidate sets,
FP-Growth Algorithm provides with the best option in order to search for the suitable candidates based on the
support count, which also enhances the performance and efficiency . The FP Growth Algorithm core is based on
the storing of the frequent data items into a special type of data structure i.e. Frequent Pattern Tree(FP-tree).
Here along with frequent items, a mapping of the items is also stored for faster access and better results.
Limitations
2. A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining
DOI: 10.9790/0661-17633943 www.iosrjournals.org 40 | Page
Despite of being one of the time efficient algorithm, this algorithm suffers from following limitations.
a) The database is scanned twice for two times for the construction of FP Tree.
b) The making of FP Tree using this algorithm takes a lot of time.
III. Proposed Methodology
The proposed algorithm is a hybrid of a modified FP Tree creation algorithm i.e FP Split and Apriori
Growth mining algorithm. This proposed algorithm can be explained using two phases.
The first phase constructs the FP Split Tree which is more efficient way to create candidate sets than
FP Tree since the latter involves two complete scans of the database while the former does it once. This impacts
the efficiency almost 2 times better. More over the FP Split Tree created by proposed hybrid algorithm involves
lesser use of pointers as here each node is not linked in the Tree to its predecessor and successor. Rather here a
header list is maintained separately which maintains a list separately for each of the pages which points to the
occurrence of these items in the final tree created.
The second phase involves mining the FP Split tree created using the Apriori growth algorithm. This
algorithm is more efficient than FP Growth as it does not involve recreating the FP Split trees repeatedly every
time in recursion as in FP Growth algorithm thereby reducing the time involved.
Phase 1: Tree construction using FP-Split Tree Algorithm.
Step-1. The database is scanned to create an equivalence class of items. Let the equivalence class of item be
ECi= {Tid | Tid are the identifier of transaction ti; i is an item of ti).
Step-2. In step 2, support is calculated in order to filter out the non-frequent items. The support of each item I-
refers to the number of records contained in the equivalence class ECi. Let |ECLi| denote the support of the
equivalence class ECi. After support calculation, items having supports below the predefined minimum support
are deleted from the set.
Step-3. In step 3, firstly frequent items are generated; secondly, the equivalence class of item is converted into
nodes for the construction of FP-split tree. Moreover, in order to facilitate tree traversal, a header table is built in
advanced so that each item can point to its first occurrence in the FP-split tree.
The node structure of FP Split tree is as follows:-
Content Count Link_Sibling
List
Link_Child
Table 1: Node Structure for FP Split Tree
In Table 1, Count represents the support count, Link_Sibling represents pointer linkage to the sibling
nodes, Link_Child represents the pointer linkage to the child nodes and content represents the frequent item set.
Step-4. This step starts the beginning of the FP Split tree construction; firstly a dummy root is created.
Step-5. The nodes are added into the FP Split tree on the basis of the four rules. These four rules are , where x
stands for a specific node in the FP Split tree.
Rule I:
If ( x is root and x.Link-child= null ) Then
x.link-child<= n
Else
Call Compare (x.Link_childList, n.list )
End if
Rule 2:
If ( n.list c x.list and x.Link-child == null ) Then
x.Link-child<= n
else
Call Compare (x.link-child.List, n.List )
End if;
Rule 3:
If ( n.List n x.List== 0 and x.Link-siblings== null) Then
x.Link-sibling <=n
else
Call Compare (x.Link-child.List, n.List )
End if;
3. A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining
DOI: 10.9790/0661-17633943 www.iosrjournals.org 41 | Page
Rule 4:
If (x.List n n.List # 0 and x.List - n.List<#0 ) Then
Call split ( n ) and return two nodes nl and n2
End if
On the basis of above four rules the new node is compared different nodes like root node, child node
and sibling node and is added accordingly into the FP Split tree.
Finally the Tree so created is ready for mining using Apriori Growth Algorithm.
Phase 2:- Tree Mining using Apriori Growth Algorithm.
In order to perform the mining by Apriori Growth aglgorithm firstly the candidates are generated using a
candidate set algorithm.
Candidate set Algorithm
Step 1. List1=k-1 frequent item dataset from Data
Step 2. N=size(list1)
Initialize mylist as a blank list to contain generated frequent item dataset
Step 3. Repeat for I=1 to n-1
Step 4. Repeat for j=I+1 to n
Step 5 l1=list1[I]
Step 6 l2=list1[j]
Step 7 Remove the last elements from l1 and l2
Step 8 if l2 is a subset of l1 then
Flist=append last element of l2 at end of l1
[end of while]
Step 9. If count(flist)>=supp then
Add flist to mylist
Else
return null
Step 10 end
New Apriori Growth Algorithm
Input: pagelist2: list of pages with equivalence class satisfying support count
Tree: Nodes of tree as a list.
Sup: minimum support
Output: frequent item sets „data‟
The algorithm is implemented with the following steps:-
Step 1. Repeat following step while scanning pagelist2 till end
▪ Create list containing single item from pagelist2
▪ Add this list to mylist1
[End of repeat]
Step 2. Add mylist1 to data
Step 3 Repeat for k=2,3,4,….
Step 4. Ck=getCandidate(k)
Step 5. If [Ck]=0 then
Goto step 6
Else
Add Ck to data
Step 6. End
Finally, we have the mined item sets in the pagelist2 data structure.
IV. Implementation Results
Output Generated is as follows:
The Frequent sets generated for threshold value 3 are as follows:
The itemsets generated for the support count 1 for the are:-
[[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21],
[22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41],
[42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61],
[62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73]]
4. A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining
DOI: 10.9790/0661-17633943 www.iosrjournals.org 42 | Page
The itemsets generated for the support count 2 are:-
[[0, 39], [0, 49], [1, 39], [1, 49], [2, 39], [7, 39], [9, 39], [12, 39], [12, 49], [13, 39], [14, 39], [23, 39], [29, 39],
[29, 49], [30, 39], [39, 41], [39, 48], [39, 49], [39, 55], [39, 65], [39, 70], [39, 72], [41, 49], [48, 49], [49, 65],
[49, 72]]
The itemsets generated for the support count 3 are:-
[[0, 39, 49], [1, 39, 49], [12, 39, 49], [29, 39, 49], [39, 41, 49], [39, 48, 49], [39, 49, 65], [39, 49, 72]]
The algorithm cannot move further for support count 4, as no more frequent items are observed.
Comparison of proposed algorithm with fp tree.
Figure 2: Number of records(number) vs timespan(milliseconds)
The graph in figure 2 shows the comparison between FP Tree and FP Split tree methods for support
count 3. We can see that the time taken by proposed algorithm is always better than the traditional method. The
difference would be more clearly visible if we get logs of few days time for some website where the incoming
traffic is also more frequent. The proposed algorithm has been tested on logs received from Kurukshetra
University website for only 29 minutes. The more is the number of records, the better visible is the difference
between the efficiency of the two algorithms.
The data from two algorithms when averaged, it demonstrated the effectiveness of our proposed
methodology. The efficiency of this technique as against traditional method is found to more efficient according
to time consumed for execution.
Figure 3: Support count(number) vs timespan (milliseconds)
The graph in figure 3 shows the comparison between FP Tree and FP Split tree methods for different
support counts for a fixed no. of records. This comparison can also be seen with the table 2 as follows:-
Support FP tree time Proposed Algorithm
Time
2 52 38
3 46 29
4 61 32
5 54 27
Table 2: Comparing the FP tree and Hybrid algorithm in time(milliseconds).
V. Conclusion
Web usage mining refers to the use the access logs from a web server to study the usage pattern of
clients. This research discusses different techniques for content mining on website logs. The two main methods
in this context- Apriori and FP Tree method are the traditional methods. The most commonly used Apriori
5. A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining
DOI: 10.9790/0661-17633943 www.iosrjournals.org 43 | Page
algorithm had a major disadvantage of performing multiple database scans for candidate set generation. The FP
Tree structure solved this problem by restricting the database scans to two times. But the FP growth algorithm
was very complicated and time consuming since it recursively created trees at every step during frequent item-
set generation. The FP-Split algorithm further improved candidate set generation by doing a single scan of the
date for candidate generation. The Apriori growth algorithm when used for mining the FP Tree performed better
in frequent item-set generation. So we proposed here a hybrid technique for web usage mining using FP Split
Tree and Apriori Growth algorithm. The hybrid algorithm was programmed using Java language and logs from
kurukshetra university website were used to demonstrate the validity and effectiveness of proposed technique.
The results showed that the proposed algorithm performed more efficiently than the traditional method.
This method can be used in association mining at many other applications like WSN, Social Network
behavioral mining etc. In future, we plan to further improve the efficiency by reducing the complexity involved
in creating FP Split tree which is certainly better than FP Tree but still can be worked upon for further
improvement.
References
[1] Chin Fewng Lee and Tsung-HsienShen, “An FP-split method for fast association rules mining”, IEEE 2005. Pp. 459-464.
[2] Bo Wu, Defu Zhang, QihuaLan, JieminZheng, “An Efficient Frequent Patterns Mining Algorithm based on Apriori Algorithm and
the FP-tree Structure”, Third 2008 International Conference on Convergence and Hybrid Information Technology, IEEE 2008.
[3] Anupam Joshi, Tim Finin, Akshay Java, Anubhav Kale, and PranamKolari, “Web (2.0) Mining: Analyzing Social Media”, IEEE
2008.
[4] K. R. Suneetha, Dr. R. Krishnamoorthi, “Identifying User Behavior by Analyzing Web Server Access Log File”, IJCSNS
International Journal of Computer Science and Network Security. VOL.9 No.4, April 2009
[5] Mehdi Heydari, Raed Ali Helal, Khairil Imran Ghauth, “A Graph-Based Web Usage Mining Method Considering Client Side
Data”, 2009 International Conference on Electrical Engineering and Informatics, 2009.
[6] HuipingPeng, “Discovery of Interesting Association Rules Based on Web Usage Mining”, 2010 - International Conference on
Multimedia Communications.
[7] MajaDimitrijevic, TanjaKrunic, “Association rules for improving website effectiveness: case analysis”, Online Journal of Applied
Knowledge Management Volume 1, Issue 2, 2013
[8] Kirti S. Patil, Sandip S. Patil, “Sequential Pattern Mining Using Apriori Algorithm & Frequent Pattern Tree Algorithm”, IOSR
Journal of Engineering (IOSRJEN) Vol. 3, Issue 1 (Jan. 2013), Pp. 26-30