Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm

The topic of Particle Swarm Optimization (PSO) has recently gained popularity. Researchers has used it to solve difficulties related to job scheduling, evaluation of stock markets and association rule mining optimization. However, the PSO method often encounters the problem of getting trapped in the local optimum. Some researchers proposed a solution to over come that problem using combination of PSO and Cauchy distribution because this performance proved to reach the optimal rules. In this paper, we focus to adopt the combination for solving association rule mining (ARM) optimization problem in numerical dataset. Therefore, the aim of this research is to extract the rule of numerical ARM optimization problem for certain multi-objective functions such as support, confidence, and amplitude. This method is called PARCD. It means that PSO for numerical association rule mining problem with Cauchy Distribu- tion. PARCD performed better results than other methods such as MOPAR, MODENAR, GAR, MOGAR and RPSOA

IDENTIFIKASI KETIDAKSESUAIAN HSE MENGGUNAKAN ASSOCIATION RULE MINING PADA PROYEK INFRASTRUKTUR DI MAKASSAR

Proyek konstruksi melibatkan banyak peserta melakukan berbagai macam kegiatan yang direncanakan. Masing-masing kegiatan diatur dalam sebuah peraturan, baik oleh pihak internal maupun pihak eksternal. Salah satu yang menyita perhatian dalam sebuah proyek adalah sistem manajemen kesehatan dan keselamatan kerja. Permasalahan utama dalam sistem manajemen kesehatan dan keselamatan kerja adalah penerapan yang tidak konsisten sehingga sering kali ditemukan adanya ketidaksesuaian atau yang biasa disebut Non Conformity. Penelitian ini menggunakan pendekatan Association Rule Mining untuk menemukan pola ketidaksesuaian tersebut. Diperoleh empat rule pola ketidaksesuaian dengan menggunakan nilai min. Support 10% dan min  Confidence 70%. Hasil penelitian menunjukkan bahwa metode ini dapat menemukan pola ketidaksesuaian dalam sistem manajemen kesehatan dan keselamatan kerja dengan harapan dapat digunakan upaya mencegah kegagalan proyek

Study on An improvement of Numerical Association Rule Extraction for Multi-Objective Optimization Problem (Case studi: Bioelectric Potential Data)

13301甲第4824号博士（工学）金沢大学博士論文要旨Abstract 以下に掲載：Sensors and Materials 30(7) pp.1509-1516 2018. MY Tokyo. 共著者：Imam Tahyudin, Hidetaka Namb

Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only. © 2008 Elsevier Ltd. All rights reserved

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

PENERAPAN MULTI-LEVEL ASSOCIATION RULE MINING PADA ANALISA KECACATAN PROYEK KONSTRUKSI

Proyek konstruksi melibatkan banyak peserta (multiparties) untuk melakukan berbagai macam kegiatan yang direncanakan. Masing-masing peserta saling berinteraksi satu sama lain sehingga semua pekerjaan yang sudah dijadwalkan bisa selesai dikerjakan. Permasalahan utama dalam pelaksanaan proses konstruksi adalah ketidakefisienan. Ketidakefisienan tersebut berupa penggunaan sumber daya yang tidak menghasilkan nilai seperti yang diharapkan. Sehingga dalam pelaksanaannya seringkali ditemukan adanya cacat konstruksi. Pekerjaan yang cacat menyebabkan adanya keterlambatan dan pembengkakan biaya, serta memicu adanya sengketa antar pelaksana pada masa konstruksi dan operasi. Untuk mengatasi permasalahan tersebut, perlu dilakukan identifikasi faktor-faktor penyebab kecacatan dalam proyek konstruksi. Dengan mengetahui faktor-faktor tersebut, diharapkan pihak menejemen dapat mengontrol dan mengendalikan pekerjaan. Penelitian ini menggunakan pendekatan Multilevel Association Rule Mining untuk menemukan pola kecacatan tersebut. Hasil penelitian menunjukkan bahwa metode ini dapat menemukan pola kecacatan dalam industri konstruksi yang kemudian digunakan sebagai dasar pembuatan mitigation plan dalam upaya mencegah kerugian perusahaan.Kata Kunci : multi level association rule mining, kecacatan proyek konstruks

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features

Discovering gene association networks by multi-objective evolutionary quantitative association rules

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge.Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02Junta de Andalucía P11-TIC-752