ANALISIS DAN IMPLEMENTASI METODE HYBROD OF K-HARMONIC MEANS AND CAT SWARM OPTIMIZATION

ABSTRAKSI: Dalam pengembangan penggunaannya, data memiliki perkembangan yang pesat dimana kumpulan data tersebut memiliki informasi yang dapat diolah untuk mendapatkan pengetahuan namun kebanyakan tidak dimanfaatkan secara maksimal karena jumlah yang melampui batas. Untuk itu diperlukan suatu cara untuk mengolah data untuk mendapatkan manfaat dari data itu, salah satunya dengan data mining. Dalam data mining, terdapat salah satu metode yang sering digunakan yaitu clustering. Clustering merupakan pengelompokan objek berdasarkan kemiripan antar objek. Dengan menggunakan metode clustering, banyak permasalahan yang dapat ditemukan pola untuk ditemukan kecenderungan tertentu dari data tersebut. Tugas akhir ini mengimplementasikan suatu metode clustering, yaitu k-harmonic means, cat swarm optimization dan hybrid of k-harmonic means and cat swarm optimization. Pada pengembangan sebelumnya, k-harmonic means memiliki kemampuan yang handal dalam mengatasi permasalahan pada k-means. Namun, k-harmonic means terkadang menemukan solusi secara cepat dimana dapat dikatakan solusi tersebut bukan solusi yang baik. Sehingga, dapat dikatakan k-harmonic means mengalami local optima yang buruk. Oleh karena itu, pada tugas akhir ini dilakukan hybrid k-harmonic means dengan cat swarm optimization dengan tujuan untuk meminimalisasi pencapaian local optima yang buruk pada k-harmonic means. Pengujian yang dilakukan untuk melihat kualitas dari sistem yaitu menggunakan f-measure. Sedangkan, untuk melihat kualitas dari clustering dilakukan dengan melihat nilai objective function dan silhouette coeficient. Objective function dan jumlah iterasi dapat digunakan sebagai indikator apakah suatu metode mengalami local optima yang buruk atau baik. Berdasarkan pengujian yang sudah dilakukan, k-harmonic means menghasilkan hasil clustering yang tidak jauh berbeda dengan hybrid of k-harmonic means and cat swarm optimization. Namun dapat terlihat dari objective function dan jumlah iterasi yang dihasilkan, hybrid of k-harmonic and cat swarm optimization menghasilkan nilai yang lebih kecil dibandingkan dengan k-harmonic means.Kata Kunci : data mining, clustering, k-harmonic means, cat swarm optimization, hybrid of k-harmonic means and cat swarm optimization, f-measure, objective function, silhouette coeficientABSTRACT: In developing its use, the data has a rapid development in which the data set has information that can be processed to gain knowledge but most are not fully utilized because of the amount that exceeded the limit. For that we need a way to process data in order to get benefits from data, one through with data mining. In data mining, there is one method often used, that is clustering. Clustering is and objectst grouping based on similarity between objects. By using clustering methods, many problem can be found each pattern of data to be found a certain tendency of data. This thesis implements a clustering method, namely K-harmonic means, Cat swarm optimization and the hybrid of K-harmonic means and Cat swarm optimization. In the previous development, K-harmonic means has been proveb to have a reliability to overcome problems of K-means. However, K-harmonic means sometimes find a quick solution that that isn’t a good solution. So that, it can be said K-harmonic means faces a bad local optima. Therefore, this thesis use a hybrid of K-hamonic means with cat swarm optimization in order to minimize the achievement of local optima. The test are performed to see the quality of system using f-measure. By measuring the quality of clustering, we can see from objective function and silhouette coefficient. Objective function and number of iterations are used to see a bad or good local optima. Based on testing that has been done, k-harmonic means clustering produces results that are not much different than hybrid of k-harmonic means. However it can be seen from objective function and number of iterations of hybrid of k-harmonic means and cat swarm optimization that is smaller than k-harmonic meansKeyword: data mining, clustering, k-harmonic means, cat swarm optimization, hybrid of k-harmonic means and cat swarm optimization, f-measure, objective function, silhouette coeficien

A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data

In this paper a hybrid forecasting technique that integrates Cat Swarm optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) with Fuzzy Time Series (FTS) forecasting is presented. In the three stages of FTS, CSO-C found application at the fuzzification module where its efficient capability in terms of data classification was utilized to neutrally divide the universe of discourse into unequal parts. Then, disambiguated fuzzy relationships were obtained using Fuzzy Set Group (FSG). In the final stage, PSO was adopted for optimization; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from FSG. The forecasting results showed that the proposed method outperformed other existing methods; using RMSE and MAPE as performance metrics.            

Clustering as an example of optimizing arbitrarily chosen objective functions

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison

LightDock: a new multi-scale approach to protein–protein docking

Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness. This work is partially supported by the European Union H2020 program through HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaciói Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes