Search Results - (( based optimization based algorithm ) OR ( pattern generation mining algorithm ))

Social media mining: a genetic based multiobjective clustering approach to topic modelling by Alfred, Rayner, Loo, Yew Jie, Obit, Joe Henry, Lim, Yuto, Haviluddin, Haviluddin, Azman, Azreen

“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
Get full text
Get full text

Social media mining: a genetic based multiobjective clustering approach to topic modelling by Rayner Alfred, Loo Yew Jie, Joe Henry Obit, Yuto Lim, Haviluddin Haviluddin, Azreen Azman

“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
Get full text
Get full text
Get full text
Get full text

Evaluation and optimization of frequent association rule based classification by Izwan Nizal Mohd Shaharanee, Jastini Jamil

“…Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. …”
Get full text
Get full text
Get full text

Multi-objective Binary Clonal Selection Algorithm In The Retrieval Phase Of Discrete Hopfield Neural Network With Weighted Systematic Satisfiability by Romli, Nurul Atiqah

“…A Binary Clonal Selection Algorithm is being proposed to ensure optimal generation of the superior final neuron states. …”
Get full text
Get full text

Data normalization techniques in swarm-based forecasting models for energy commodity spot price by Yusof, Yuhanis, Mustaffa, Zuriani, Kamaruddin, Siti Sakira

“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
Get full text
Get full text
Get full text

Evolutionary-based feature construction with substitution for data summarization using DARA by Sia, Florence, Alfred, Rayner

“…This paper proposes an evolutionary-based feature construction approach namely Fixed-Length Feature Construction with Substitution (FLFCWS) to address the problem by means of optimizing the feature construction for relational data summarization. …”
Get full text
Get full text
Get full text

A frequent pattern mining algorithm based on FP-growth without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

“…An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. …”
Get full text
Get full text

A frequent pattern mining algorithm based on FP-growth without generating tree by Tohid, Hossein, Ibrahim, Hamidah

“…An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows.First, it compresses the database representing frequent items into a frequent-pattern tree, or FP-tree, which retains the itemset association information. …”
Get full text
Get full text
Get full text

A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

“…Frequent pattern mining is one of the active research themes in data mining. …”
Get full text
Get full text

Using unique-prime-factorization theorem to mine frequent patterns without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

“…In this study we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. …”
Get full text
Get full text
Get full text

Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

“…The mined frequent patterns are then used in generating association rules. …”
Get full text
Get full text

Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset by Julaily Aida, Jusoh, Wan Aezwani, Wan Abu Bakar, Mustafa, Man

“…The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. …”
Get full text
Get full text

Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

“…The proposed method, XiFLEX has been implemented using two different techniques (java based & XQuery) and compared with the original FLEX algorithm in its basic implementation and the Apriori algorithm for frequent patterns generation. …”
Get full text
Get full text

MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, khan

“…In this paper we proposed an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products. …”
Get full text
Get full text

Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm by G. Noma, Nasir, Mohd Khanapi, Abd Ghani

“…We use frequent pattern growth (FP-Growth) algorithm in the data processing step to build the FP-tree data structure and mine it for frequents itemsets. …”
Get full text
Get full text

MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, Khan

“…In this paper we proposed an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products. …”
Get full text
Get full text

Sequential pattern mining on library transaction data by Sitanggang, Imas Sukaesih, Husin, Nor Azura, Agustina, Anita, Mahmoodian, Naghmeh

“…This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. …”
Get full text
Get full text

Mining dense data: Association rule discovery on benchmark case study by Bakar, W.A.W.A., Saman, M.D.M., Abdullah, Z., Jalil, M.A., Herawan, T.

“…In this article, we present comparison result between Apriori and FP-Growth algorithm in generating association rules based on a benchmark data from frequent itemset mining data repository. …”
Get full text

Exploratory analysis with association rule mining algorithms in the retail industry / Alaa Amin Hashad ... [et al.] by Hashad, Alaa Amin, Khaw, Khai Wah, Alnoor, Alhamzah, Chew, Xin Ying

“…The proposed method is based on comparing two algorithms: Apriori and Frequent Pattern Growth (FP- Growth). …”
Get full text
Get full text

Comparative study of apriori-variant algorithms by Mutalib, Sofianita, Abdul Subar, Ammar Azri, Abdul Rahman, Shuzlina, Mohamed, Azlinah

“…One of data mining methods is frequent itemset mining that has been implemented in real world applications, such as identifying buying patterns in grocery and online customers’ behavior.Apriori is a classical algorithm in frequent itemset mining, that able to discover large number or itemset with a certain threshold value. …”
Get full text
Get full text
Get full text