Microarray time-series data clustering via gene expression profile alignment

Clustering gene expression data given In terms of time-series is a challenging problem that imposes its own particular constraints, namely, exchanging two or more time points is not possible as it would deliver quite different results and would lead to erroneous biological conclusions. In this thesis, clustering methods introducing the concept of multiple alignment of natural cubic spline representations of gene expression profiles are presented. The multiple alignment is achieved by minimizing the sum of integrated squared errors over a time-interval, defined on a set of profiles. The proposed approach with flat clustering algorithms like k-means and EM are shown to cluster microarray time-series profiles efficiently and reduce the computational time significantly. The effectiveness of the approaches is experimented on six data sets. Experiments have also been carried out in order to determine the number of clusters and to determine the accuracies of the proposed approaches

Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export

Natural rubber is one of the pillars of Indonesia's export commodities. However, over the last few years, the export value of natural rubber has decreased due to an oversupply of this commodity in the global market. To overcome this problem, it is possible to predict the volume of Indonesia natural rubber exports. Predicted values can also help the government to compile market intelligence for natural rubber commodities periodically. In this study, the prediction of the export volume of natural rubber was carried out using the Automatic Clustering as an interval maker in the Fuzzy Time Series or usually called Automatic Clustering and Fuzzy Logical Relationship (ACFLR). The data used is 51 data per year from 1970 to 2020. The purpose of this study is to predict the volume of Indonesia natural rubber exports and compare the prediction results between the Automatic Clustering and Fuzzy Logical Relationship (ACFLR) and Chen's Fuzzy Time Series. The results showed that there was a significant difference between the two methods, ACFLR got 0.5316% MAPE with  and Chen's Fuzzy Time Series model got 8.009%. Show that the ACFLR method performs better than the pure Fuzzy Time Series in predicting volume of Indonesia natural rubber exports

Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm

Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results

IMPLEMENTASI ALGORITMA AUTOMATIC CLUSTERING DAN FUZZY TIME SERIES UNTUK PERAMALAN PENJUALAN SEPEDA MOTOR

Dalam suatu rangkaian proses bisnis, penjualan merupakan ujung tombak keberhasilan sebuah perusahaan. Untuk mendapatkan hasil penjualan yang baik, dibutuhkan suatu peramalan penjualan yang baik pula. Permasalahan dalam menghasilkan ramalan yang akurat adalah pemilihan metode peramalan yang tepat. Dalam penelitian ini, dilakukan penelitian terhadap peramalan penjualan sepeda motor di Indonesia dengan menggunakan metode fuzzy time series yang dikombinasikan dengan penentuan interval menggunakan algoritma automatic clustering, dengan judul penelitian “Implementasi algoritma automatic clustering dan fuzzy time series untuk peramalan penjualan sepeda motor”. Penelitian ini mengggunakan data historis penjualan sepeda motor dalam 10 tahun terakhir. Untuk mengetahui tingkat kesalahan dari peramalan, dihitung dengan menggunakan Means Percentase Error (MPE) dan Means Absolute Percentase Error (MAPE). Hasil kesalahan peramalan dengan menggunakan metode fuzzy time series dan automatic clustering pada bulan Januari MAPE sebesar 3,26%, MPE sebesar 0,65%, hasil tersebut lebih baik daripada menggunakan metode fuzzy time series tanpa automatic clustering dengan hasil MAPE 3,60%, dan MPE -1,99%. Hasil peramalan menggunakan metode fuzzy time series dan automatic clustering dalam periode satu tahun menunjukkan MAPE sebesar 2,15% dan MPE sebesar 0,19%.;--- In a series of business processes, spearheading the success of a company is the number of sales. To get good sales results, we need a good sales forecasting anyway. The problems in producing accurate forecasts are correct forecasting method selection. In this study, research is conducted on forecasting sales of motorcycles in Indonesia by using fuzzy time series combined with the determination of the interval using automatic clustering algorithm, with the title "The implementation of automatic clustering algorithm and fuzzy time series forecasting sales of motorcycles". This study uses historical data of motorcycle sales in the last 10 years. To determine the level of forecasting error, calculated using the Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting error by using fuzzy time series and automatic clustering in January, MAPE of 3,26% and MPE 0.65%, the result is better than using fuzzy time series without automatic clustering which error result MAPE of 3,60% and MPE of 1.99% below the actual data. Forecasting results using fuzzy time series and automatic clustering within one year showed a MAPE of 2.15% and of MPE 0.19%

Metode Automatic clustering-fuzzy logical relationships pada Peramalan Jumlah Penduduk di Kota Makassar

Abstrak. Penelitian ini merupakan penerapan metode automatic clustering-fuzzy logical relationships unruk meramalkan jumlah penduduk di Kota Makassar menggunakan data sekunder BPS Kota Makassar yang bertujuan memprediksi jumlah penduduk  tahun 2017-2021. Penelitian diawali dengan penentuan panjang interval, nilai tengah panjang interval, membuat relasi logika fuzzy, fuzzifikasi, defuzzifikasi, dan menghitung nilai error hasil ramalan dengan metode Mean Absolute Percentage Error. Hasil penelitian ini menunjukkan bahwa ramalan jumlah penduduk di Kota Makassar dari tahun 2016 ke 2017 meningkat, tahun 2017 sampai tahun 2019 menurun, dan pada tahun 2019-2021 meningkat dengan keakuratan yang sangat bagus.Kata kunci:Automatic clustering-fuzzy logical relationships, Fuzzy Time Series,TeoriFuzzyAbstract.This research is the application of the forecasting method of fuzzy time series which is the method of automatic clustering fuzzy-logical relationships in forecasting the population of Makassar City using secondary data from BPS Makassar city which aims to predicting the population in year 2017-2021. The discussion starting from the determination of the length of the interval, determining the value of the middle length interval, making relations of fuzzy logic, fuzzification, defuzzification, and calculating the error value of the forecasting result by using the method of Mean Absolute Percentage Error. The result of this research shows that the predictions of the population of Makassar City from 2016 to 2017 increased, from 2017 to 2019 decreased, and in 2019-2021 increased with the very good accuracy. Keywords:Automatic Clustering-Fuzzy Logical Relationships, Fuzzy Time Series,Fuzzy Theor

Development of a Realistic Driving Cycle Using Time Series Clustering Technique for Buses: Thailand Case Study

Realistic driving cycles for Thailand’s road conditions were studied only for the pollution problems of passenger cars and light-duty trucks in urban areas of the metropolitan region. Such driving cycles did not cover rural areas and the in-use operation of buses. Furthermore, such created methods of the driving pattern were very irregular and complicated for chassis dynamometer operation. To propose a new method for a realistic driving cycle, the development of the route traffic for bus transportation in rural areas using a time series clustering technique is indicated. As a case study, this method was applied to collect the driving data on the 323 route in Kanchanaburi province using on-board measurement. The selection procedure of suitable speed and interval time for driving cycle construction was revealed. Similarity of driving characteristics was identified with clustering technique for each time duration to decide the best driving cycle. In conclusion, the frequency of speed ranges from entire trips is 30-40 km/h with the highest ratio of deceleration time to the entire trip. Furthermore, discrete average speeds at each time point with 40 seconds of interval time are the best choice related to the real driving condition in this case study

Korespondensi The New Approach Optimization Markov Weighted Fuzzy Time Series using Particle Swarm Algorithm

Markov Weighted Fuzzy Time Series is a forecasting method that applies fuzzy logic to form linguistic variables from existing data. The formation of linguistic variables makes it possible for the forecasting process to be more accurate by considering the uncertainty aspect in decision-making. Its formation is started by grouping the data into a certain number of clusters. The next step is fuzzification, transition matrix formation, and defuzzification for forecasting. In the process of grouping, the existing data will be grouped into several clusters so that it results in the interval length of each cluster. One of the problems of this grouping is the absence of a base standard in the clustering process so it is prone to have a different value in forecasting accuracy. The difference in the number of the class or interval length will result in different accuracy even though the clustering method that is used is the same. In this study, the author proposes the idea of using Particle Swarm Optimization to improve the interval length. The initial interval that is already obtained through the K-means clustering algorithm will be evaluated using the Particle Swarm Optimization method so that it will have a new interval that later will be used in the fuzzification process and forecasting. The accuracy of forecasting can be calculated by using Mean Absolute Percentage Error from Markov Weighted Fuzzy Time Series conventional method and Markov Weighted Fuzzy Time Series method with Particle Swarm Optimization. The result of this study gives an improvement in error value from 8.03% to 5.88%