A hybrid genetic algorithm–fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals

2013 ◽  
Vol 17 (10) ◽  
pp. 1787-1796 ◽  
Author(s):  
Dan Li ◽  
Hong Gu ◽  
Liyong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Data Clustering ◽  
Incomplete Data ◽  
Nearest Neighbor ◽  
Hybrid Genetic Algorithm ◽  
Fuzzy C Means

A Fuzzy C-Means Approach for Incomplete Data Sets Based on Nearest-Neighbor Intervals

2013 ◽  
Vol 411-414 ◽  
pp. 1108-1111 ◽  
Author(s):  
Dan Li ◽  
Chong Quan Zhong ◽  
Shi Qiang Wang
Keyword(s):  
Pattern Recognition ◽  
Missing Data ◽  
Data Clustering ◽  
Incomplete Data ◽  
Nearest Neighbor ◽  
Experimental Results ◽  
Data Sets ◽  
Fuzzy C Means

Partially missing data sets are a prevailing problem in pattern recognition. In this paper, the problem of clustering incomplete data sets is considered, and missing attribute values are imputed by the centers of corresponding nearest-neighbor intervals. Firstly, the algorithm estimates the nearest-neighbor intervals of missing attribute values by using the attribute distribution information of the data sets sufficiently. Secondly, the missing attribute values are imputed by the center of the intervals so as to clustering incomplete data sets. The proposed algorithm introduces the nearest neighbor information into incomplete data clustering, and the comparisons of the experimental results for two UCI data sets demonstrate the capability of the proposed algorithm.

scholarly journals Solving open travelling salesman subset-tour problem through a hybrid genetic algorithm

2021 ◽  
pp. 209-222 ◽  
Author(s):  
Purusotham Singamsetty ◽  
Jayanth Kumar Thenepalle ◽  
Balakrishna Uruturu
Keyword(s):  
Genetic Algorithm ◽  
Nearest Neighbor ◽  
Hybrid Genetic Algorithm ◽  
Subset Selection ◽  
Future Research ◽  
Travelling Salesman ◽  
Optimal Sequence ◽  
Benchmark Instances ◽  
Selected Subset ◽  
Mutation Strategies

In open travelling salesman subset-tour problem (OTSSP), the salesman needs to traverse a set of k (≤n) out of n cities and after visiting the last city, the salesman does not necessarily return to the central depot. The goal is to minimize the overall traversal distance of covering k cities. The OTSSP model comprises two types of problems such as subset selection and permutation of the cities. Firstly, the problem of selection takes place as the salesman’s tours do not contain all the cities. On the other hand, the next problem is about to determine the optimal sequence of the cities from the selected subset of cities. To deal with this problem efficiently, a hybrid nearest neighbor technique based crossover-free Genetic algorithm (GA) with complex mutation strategies is proposed. To the best of the author’s knowledge, this is the first hybrid GA for the OTSSP. As there are no existing studies on OTSSP yet, benchmark instances are not available for OTSSP. For computational experiments, a set of test instances is created by using TSPLIB. The extensive computational results show that the proposed algorithm is having great potential in achieving better results for the OTSSP. Our proposed GA being the first evolutionary-based algorithm that will help as the baseline for future research on OTSSP.

scholarly journals PENENTUAN JARINGAN LOGISTIK MENGGUNAKAN FUZZY C-MEANS DAN MINIMUM SPANNING TREE BERBASIS GENETIC ALGORITHM

2019 ◽  
Vol 9 (1) ◽  
pp. 112
Author(s):  
Shinta Tri kismanti ◽  
Andi Ard Maidhah
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Hybrid Genetic Algorithm ◽  
Fuzzy C Means ◽  
Crossover Rate ◽  
Cluster 2 ◽  
Optimal Cluster

Indonesia  sebagai negara kepulauan dengan lebih dari 17.000 pulau dengan wilayah perairan menjadi salah satu moda transportasinya. Dengan demikian sebagian besar aktivitas terjadi di wilayah perairan, diantaranya distribusi logistik. Pergerakan distribusi logistik tersebut akan menghasilkan pola rute suatu pergerakan logistik. Penentuan pola jaringan pergerakan logistik yang optimal dapat mendukung kelancaran dalam sistem pendistribusian. Pada penelitian ini penentuan pola jaringan logistik dilakukan dengan dua tahapan. Tahap pertama, akan dilakukan pengklasteran dengan menggunakan Fuzzy C-means, pengklasteran bertujuan untuk mendapatkan kelompok-kelompok pulau yang berada pada lokasi yang berdekatan. Tahap kedua setelah didapatkan hasil cluster yang optimal, dengan menggunakan minimum spanning tree berbasis hybrid genetic algorithm akan didapatkan pola jaringan yang optimal. Pola jaringan tersebut akan menghubungkan pulau yang terpilih sebagai titik pusat ke pulau-pulau disekitarnya. Hasil uji coba pada proses pengklasteran menggunakan FCM didapatkan jumlah cluster optimal sebanyak 3 cluster. Pada proses pembentukkan MST berbasis hybrid GA digunakan parameter crossover rate 0,2 dan mutation rate 0,4 dan diperoleh hasil terbaik ketika iterasi minimumnya konvergen pada solusi optimal. Cluster 1 didapatkan hasil terbaik dengan ukuran populasi 100 dan generasi maksimum 2000 dengan nilai fitness yaitu 9.41, cluster 2 dengan ukuran populasi 100 dan generasi maksimum 1000 dengan nilai fitness yaitu 14.97, dan cluster 3 dengan ukuran populasi 100 dan generasi maksimum 1000 dengan nilai fitness yaitu 17.46.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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