Discovering Regression Rules with Ant Colony Optimization

The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible

Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90

Analisis dan Implementasi Algoritma Unordered Rule Sets Ant-Miner Untuk Klasifikasi Pelanggan Potensial Perusahaan Perbankan

ABSTRAKSI: Data mining merupakan sebuah proses untuk mengeksplorasi dan menganalisis data dalam jumlah yang besar dan bertujuan untuk menemukan pola dan keteraturan dari data tersebut yang berguna dalam proses pengambilan keputusan dimasa depan. Data mining sangat cocok untuk memberikan solusi dalam permasalahan data yang besar dalam dunia perbankan. Pada tugas akhir ini dianalisis data-data pelanggan suatu perusahaan perbankan dalam jumlah yang besar dengan menggunakan metode Ant Colony Optimization untuk menemukan pola keteraturan dari data yang akan digunakan sebagai acuan bagi pihak perusahaan untuk mengembangkan perusahaan kedepannya. Pada tugas akhir ini juga diimplementasikan bagaimana pengklasifikasian pelanggan yang potensial dari data mentah dengan menggunakan Algoritma Unordered Rule Sets Ant-Miner yang berguna untuk mengefektifkan pengklasifikasian kaidah-kaidah atau aturan dalam data tersebut. Tujuan kaidah-kaidah ini adalah untuk membangun kasus kedalam satu kelas di luar kelas yang telah didefinisikan sebelumnya berdasarkan dari beberapa atribut prediksi dari kasus tersebut. Algoritma Unordered Rule Sets Ant-Miner merupakan algoritma versi terbaru dari algoritma Ant-Miner yang juga mampu membangkitkan kaidah-kaidah atau aturan dari suatu database (data training) dengan akurasi yang sangat baik pada pengujian terbaiknya. Kata Kunci : data mining, aturan klasifikasi, ant colony optimization (ACO), unordered rule sets ant-miner, pheromone, min_case_per_rule.ABSTRACT: Data mining is a process to explore and analyze data in large quantities and aims to discover patterns and regularities of the data that are useful in decision-making process in the future. Data mining is very suited to provide solutions in large data problems in the banking sector. In this final task analyzed the data of a banking enterprise customers in large numbers using Ant Colony Optimization methods for finding patterns of order that data will be used as a reference for the company to develop the company\u27s future. In this final task is to implement how the classification of potential customers from the raw data using an unordered algorithm Rule Sets Ant-Miner is useful to streamline the classification rules or regulations in the data. The purpose of these rules is to build the case into a class outside the classroom that have been previously defined on the basis of several attributes predictions of the case. Unordered Rule Sets Ant-Miner algorithm is the latest version of the Ant-Miner algorithm is also capable of generating the rules or the rules of a database (training data) with very good accuracy on the best test.Keyword: data mining, classification rule, ant colony optimization (ACO), unordered rule sets ant-miner, pheromone, min_case_per_rule

ADR-Miner: An Ant-based data reduction algorithm for classification

Classi cation is a central problem in the elds of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classi er) that can be used to predict the class of new unlabeled instances. Data preparation is crucial to the data mining process, and its focus is to improve the tness of the training data for the learning algorithms to produce more e ective classi ers. Two widely applied data preparation methods are feature selection and instance selection, which fall under the umbrella of data reduction. For my research I propose ADR-Miner, a novel data reduction algorithm that utilizes ant colony optimization (ACO). ADR-Miner is designed to perform instance selection to improve the predictive e ectiveness of the constructed classi cation models. Two versions of ADR-Miner are developed: a base version that uses a single classi cation algorithm during both training and testing, and an extended version which uses separate classi cation algorithms for each phase. The base version of the ADR-Miner algorithm is evaluated against 20 data sets using three classi cation algorithms, and the results are compared to a benchmark data reduction algorithm. The non-parametric Wilcoxon signed-ranks test will is employed to gauge the statistical signi cance of the results obtained. The extended version of ADR-Miner is evaluated against 37 data sets using pairings from fi ve classi cation algorithms and these results are benchmarked against the performance of the classi cation algorithms but without reduction applied as pre-processing. Keywords: Ant Colony Optimization (ACO), Data Mining, Classi cation, Data Reduction

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms

ANALISIS DATA MINING METODE KLASIFIKASI DENGAN ALGORITMA ACO ( ANT COLONY OPTIMIZATION ) : ANT_MINER3 Analysis Of Data Mining Classification with ACO ( Ant Colony Optimization ) Algorithm : Ant_Miner3

ABSTRAKSI: Pada saat ini, banyak perusahaan yang memiliki data dalam jumlah yang besar. Data dalam jumlah besar tersebut ternyata dapat dimanfaatkan untuk meningkatkan kinerja perusahaan. Untuk itu diperlukan proses Data Mining. Salah satu metode dalam Data Mining adalah klasifikasi. Klasifikasi bertujuan untuk memperoleh pola tertentu, dalam bentuk tree, aturan klasifikasi atau model matematis. Untuk memperoleh pola tersebut diperlukan algoritma tertentu. Salah satunya adalah dengan Ant Colony Optimization. ACO telah digunakan dalam Data Mining dan diberi nama Ant_Miner. Namun seiring perjalanan waktu, dilakukan perubahan terhadap algoritma tersebut hingga saat ini, dan dikenal dengan Ant_Miner3. Pada tugas akhir ini dianalisa pengaruh perubahan yang dilakukan pada Ant_Miner3 terhadap tingkat akurasi dan simplisitas aturan yang dihasilkan, serta parameter sistem yang mempengaruhinya. Untuk mengetahui hal tersebut dibangun perangkat lunak sebagai media pengujian algoritma Ant_Miner3, dan membandingkannya dengan hasil yang diperoleh dengan tools Data Mining See5 yang menggunakan algoritma yang sangat sering dipakai dalam Data Mining yaitu C5.0 pada dataset Breast Cancer, Tic-tac-toe, dan House Votes. Hasilnya tingkat akurasi Ant_Miner3 lebih baik daripada C5.0, sementara simplisitas aturan yang dihasilkan tidak jauh berbeda. Tingkat akurasi dapat ditingkatkan dengan menggunakan pheromone serta dengan memperbesar nilai parameter no_of_ants dan no_rules converg. Selain itu, dengan memberikan nilai parameter pheromone evaporation, max_uncovered_case dan min_cases_per_rule yang kecil juga dapat meningkatkan akurasi pheromone. Sementara simplisitas aturan dapat ditingkatkan dengan menerapkan teknik pruning, dan memberikan nilai max_uncovered_cases dan min_cases_per_rule yang besar.Kata Kunci : Data Mining, Ant Colony Optimization, Ant_Miner, Ant_Miner3ABSTRACT: Nowadays, many companies already have a big amount of data, since the datas is important to the companies, not only for current importances, but for the future needs of the companies. In fact, the datas can be used to improve the company performance. It can be done with Data Mining. Data Mining have a several methods, one of them is classification. Classification main goal is to find a special pattern, that can be formed in tree, classification rule, or mathematics formula, within the datas. In order to find this, all we have to do is applying a certain algorithm. One of that is Ant Colony Optimization. ACO have been applied in Data Mining task, called Ant_Miner. But as the time goes by, some scientist have it modified and invented a new Ant_Miner3. This Final Task analyzed the changes effect in Ant_Miner3 with the accuracy and the simplicity of rules that was found, and how system parameter effect to it’s result. For that purpose, the Ant_Miner3 software was build, and compared it to another Data Mining tools See5 which uses a well known C5.0 algorithm in classifying the Breast Cancer, Tic-tac-toe and House Votes datasets. The result shows that Ant_Miner3 have a better accuracy than C5.0, and it also have a little differences in simplicity. The accuration rate can be improved by the use of pheromone and assigning the no_of_ants with a greater number. Besides, by assigning pheromone evaporation, max_uncovered_case and min_cases_per_rule with smaller number will increase the accuracy. While the simplicity can be improved with the use of pruning technique, and assigning max_uncovered_case and min_cases_per_rule with a greater number.Keyword: Data Mining, Ant Colony Optimization, Ant_Miner, Ant_Miner3

An Automatic Programming ACO-Based Algorithm for Classification Rule Mining

In this paper we present a novel algorithm, named GBAP, that jointly uses automatic programming with ant colony optimization for mining classification rules. GBAP is based on a context-free grammar that properly guides the search process of valid rules. Furthermore, its most important characteristics are also discussed, such as the use of two different heuristic measures for every transition rule, as well as the way it evaluates the mined rules. These features enhance the final rule compilation from the output classifier. Finally, the experiments over 17 diverse data sets prove that the accuracy values obtained by GBAP are pretty competitive and even better than those resulting from the top Ant-Miner algorith