Sugeno Fuzzy Classifier for Iris Database

التصنيف يمثل بناء نموذج قادر على التنبؤ بنوع العناصر التي نوعها غير معروف. في هذا العمل, المنطق الضبابي لسوجينو مقدم لتصنيف غير مشرف عليه لقاعدة بيانات زهرة السوسن. اربعة واربعون قاعدة ضبابية استخدمت لتصنيف قاعدة البيانات هذه مع اربعة خصائص (مدخلات)  مثلت باستخدام ثلاث مجاميع ضبابية بواسطة دوال العضوية شبة المنحرف و المثلثية. نتائج نظام التصنيف المفترض برهنت كفاءته لتصنيف قاعدة البيانات باستخدام القواعد الضبابية.Classification acts building model able to predict the class of objects whose class is unknown. In this work, Sugeno fuzzy logic is presented for unsupervised classification database of iris flower. Forty four fuzzy rules are used to classify this database using 4 features (inputs) that are acted by three fuzzy sets using trapzoidal and triangular Membership functions . The proposed classification system results proved its efficiency to classify the database using fuzzy rules

A Mining Algorithm under Fuzzy Taxonomic Structures

Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting fuzzy association rules under given fuzzy taxonomic structures. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level fuzzy taxonomy to find fuzzy association rules from given transaction data sets. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexit

Aplikasi Quality Function Deployment (QFD) Untuk Meningkatkan Kualitas Produk Garmen

Setiap perusahaan menginginkan usahanya sukses dan mampu bertahan lama. Demikian halnya dengan objek penelitian ini yaitu di Industri Kecil Garmen X dan pesaing yang memproduksi garmen yang pembuatannya menggunakan mesin otomatis. Bagaimanapun industry kecil menghendaki penjualannya makin meningkat dan mampu bersaing dengan produk merk lain dengan Aplikasi Quality Function Deployment (QFD) Untuk Meningkatkan Kualitas Produk Garmen. Tujuan penelitian ini merumuskan strategi yang tepat untuk memenuhi kepuasan konsumen garmen dengan mengaplikasikan metode Quality Function Deployment (QFD) dengan mengintegrasikan Triangular Fuzzy’s Number. Berdasarkan tujuan ini maka hasil penelitian adalah bagaimana cara peningkatan kualitas garmen yang diproduksi sehingga mampu meningkatkan kepuasan konsumen. Semakin meningkatnya kepuasan konsumen maka diharapkan volume penjualan juga meningkat. Kesimpulan penelitian ini memberikan suatu perencanaan yang strategis melalui analisa QFD sehingga mampu meningkatkan kualitas produk dan kepuasan konsumen sehingga perusahaan mampu bersaing dengan produk garmen lainnya

Optimizing of Interval Type-2 Fuzzy Logic Systems Using Hybrid Heuristic Algorithm Evaluated by Classification

In this research, an optimization of the rule base and the parameter of interval type-2 fuzzy set generation by a hybrid heuristic algorithm using particle swarm and genetic algorithms is proposed for classification application. For the Iris data set, 90 records were selected randomly for training, and the rest, 60 records, were used for testing. For the Wisconsin Breast Cancer data set, the author deleted the missing attribute value of 16 records and randomly selected 500 records for training, and the rest, 183 records, were used for testing. The proposed method was able to minimize rule-base, minimize linguistic variable and produce a accurate classification at 95% with the first dataset and 98:71% with the second datase

Data Mining with Linguistic Thresholds

Abstract Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In the past, the minimum supports and minimum confidences were set at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural and understandable for human beings. This paper thus attempts to propose a new mining approach for extracting interesting weighted association rules from transactions, when the parameters needed in the mining process are given in linguistic terms. Items are also evaluated by managers as linguistic terms to reflect their importance, which are then transformed as fuzzy sets of weights. Fuzzy operations including fuzzy ranking are then used to find weighted large itemsets and association rules

An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.

Analisis Kepuasan Pasien Berdasarkan Metode Servqual – Fuzzy Di Klinik Umum Dan Bersalin Rumah Sehat Wahida

Klinik Umum dan Bersalin Rumah Sehat Wahida sebagai ujung tombak pelayanan kesehatan masyarakat memiliki komitmen untuk selalu meningkatkan pelayanan yang dibutuhkan oleh konsumen / pelanggan (pasien). Namun seiring dengan semakin mapannya klinik kesehatan memberikan dampak klinis berupa persaingan yang semakin ketat untuk mendapatkan pelanggan setia. Melalui penelitian ini dengan metode Servqual-Fuzzy, diharapkan variabel klinis mampu mengidentifikasi pelayanan yang perlu diperbaiki dan ditingkatkan. Penelitian ini menggunakan kuesioner terbuka dan tertutup yang mencakup lima dimensi kualitas layanan yaitu tangible (bukti langsung), reliability responsiveness, assurance (jaminan), dan empati. Berdasarkan hasil penelitian didapatkan bahwa variabel Jadwal Pelayanan Perawatan Tepat Setiap Hari memiliki gap terbesar 0,2072. Sedangkan dimensi reliabilitas memiliki gap terbesar yaitu 0.6542. General Clinic and Maternity Rumah Sehat Wahida as the spearhead of the public health service has a commitment to always improve the services needed by the consumer / customer (patient). But as more and more established health clinics provide clinical impact of increasing competition to gain loyal customers. Through this study the method Servqual-Fuzzy, expected clinical variables able to identify services that need to be repaired and upgraded. This study uses open and closed questionnaire that includes five dimensions of service quality that is tangible (direct evidence), reliability responsiveness, assurance (guarantees), and empathy. Based on this research, it was found that the variable service schedule Right Care Every Time The day has the largest gap 0.2072. While the dimensions of reliability has the largest gap is 0.6542

A novel rule induction algorithm with improved handling of continuous valued attributes

Machine learning programs can automatically learn to recognise complex patterns and make intelligent decisions based on data. Machine learning has become a powerful tool for data mining. A great deal of research in machine learning has focused on concept learning or classification learning. Among the various machine learning approaches that have been developed for classification, inductive learning from examples is the most commonly adopted in real-life applications. Due to non-uniform data formats and huge volume of data, it is a challenge for scientists across different disciplines to optimise the process of knowledge acquisition from data with naïve inductive learning techniques. The overarching purpose of this research is to develop a novel and efficient rule induction algorithm a learning algorithm for inducing general rules from specific examples that can deal with both discrete and continuous variables without the need for data pre-processing. This thesis presents a novel rule induction algorithm known as RULES-8 which utilises guidelines for the selection of seed examples, together with a simple method to form rules. The research also aims to improve current pruning methods for handling noisy examples. Another major concern of the work is designing a new heuristic for controlling the rule formation and selection processes. Finally, it concentrates on developing a new efficient learning algorithm for continuous output using fuzzy logic theory. The proposed algorithm allows automatic creation of membership functions and produces accurate as well as compact fuzzy sets.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A novel rule induction algorithm with improved handling of continuous valued attributes

Machine learning programs can automatically learn to recognise complex patterns and make intelligent decisions based on data. Machine learning has become a powerful tool for data mining. A great deal of research in machine learning has focused on concept learning or classification learning. Among the various machine learning approaches that have been developed for classification, inductive learning from examples is the most commonly adopted in real-life applications. Due to non-uniform data formats and huge volume of data, it is a challenge for scientists across different disciplines to optimise the process of knowledge acquisition from data with naïve inductive learning techniques. The overarching purpose of this research is to develop a novel and efficient rule induction algorithm a learning algorithm for inducing general rules from specific examples that can deal with both discrete and continuous variables without the need for data pre-processing. This thesis presents a novel rule induction algorithm known as RULES-8 which utilises guidelines for the selection of seed examples, together with a simple method to form rules. The research also aims to improve current pruning methods for handling noisy examples. Another major concern of the work is designing a new heuristic for controlling the rule formation and selection processes. Finally, it concentrates on developing a new efficient learning algorithm for continuous output using fuzzy logic theory. The proposed algorithm allows automatic creation of membership functions and produces accurate as well as compact fuzzy sets

Fuzzy set covering as a new paradigm for the induction of fuzzy classification rules

In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of imprecise'' mathematics. In the middle to late 1980's the success of fuzzy controllers brought fuzzy sets into the limelight, and many applications using fuzzy sets started appearing. In the early 1970's the first machine learning algorithms started appearing. The AQ family of algorithms pioneered by Ryszard S. Michalski is a good example of the family of set covering algorithms. This class of learning algorithm induces concept descriptions by a greedy construction of rules that describe (or cover) positive training examples but not negative training examples. The learning process is iterative, and in each iteration one rule is induced and the positive examples covered by the rule removed from the set of positive training examples. Because positive instances are separated from negative instances, the term separate-and-conquer has been used to contrast the learning strategy against decision tree induction that use a divide-and-conquer learning strategy. This dissertation proposes fuzzy set covering as a powerful rule induction strategy. We survey existing fuzzy learning algorithms, and conclude that very few fuzzy learning algorithms follow a greedy rule construction strategy and no publications to date made the link between fuzzy sets and set covering explicit. We first develop the theoretical aspects of fuzzy set covering, and then apply these in proposing the first fuzzy learning algorithm that apply set covering and make explicit use of a partial order for fuzzy classification rule induction. We also investigate several strategies to improve upon the basic algorithm, such as better search heuristics and different rule evaluation metrics. We then continue by proposing a general unifying framework for fuzzy set covering algorithms. We demonstrate the benefits of the framework and propose several further fuzzy set covering algorithms that fit within the framework. We compare fuzzy and crisp rule induction, and provide arguments in favour of fuzzy set covering as a rule induction strategy. We also show that our learning algorithms outperform other fuzzy rule learners on real world data. We further explore the idea of simultaneous concept learning in the fuzzy case, and continue to propose the first fuzzy decision list induction algorithm. Finally, we propose a first strategy for encoding the rule sets generated by our fuzzy set covering algorithms inside an equivalent neural network