Comparison of fuzzy clustering methods in economic freedom ranking in Asia-Pacific

Economic freedom can be defined as freedom in which individuals can perform their economic activities freely without being exposed to the pressures and constraints. The aim of the studies on the classification of countries according to their economic freedoms is to determine the place of the countries in the world or in the continent where they are located. In this way, the status of the countries with sustainable growth and high welfare is determined. In this study, it is aimed to rank Asian countries according to economic freedom data. In contrast to many classifications and sorting studies, the present study attempts to determine the best sorting method by comparing multiple methods. As a result of the economic freedoms published by the Heritage Foundation every year, the conditions of Asian countries between 2015-2019 were determined. Fuzzy C-Means, Gath-Geva and Gustafson-Kessel methods, which are the three most commonly used methods, were used in the fuzzy clustering analysis. The results obtained from all fuzzy clustering methods were compared and interpreted with the results of the Heritage Foundation year by year. According to all analysis results, it can be said that the Fuzzy C-means method is more successful for Economic Freedom data and classification studies. According to the Fuzzy C-Means method, the three best Asian countries were Hong Kong, New Zealand and Australia respectivel

An Image Retrieval System Based on the Color Complexity of Images

The fuzzy color histogram (FCH) spreads each pixel's total membership value to all histogram bins based on their color similarity. The FCH is insensitive to quantization errors. However, the FCH can state only the global properties of an image rather than the local properties. For example, it cannot depict the color complexity of an image. To characterize the color complexity of an image, this paper presents two image features -- the color variances among adjacent segments (CVAAS) and the color variances of the pixels within an identical segment (CVPWIS). Both features can explain not only the color complexity but also the principal pixel colors of an image. Experimental results show that the CVAAS and CVPWIS based image retrieval systems can provide a high accuracy rate for finding out the database images that satisfy the users' requirement. Moreover, both systems can also resist the scale variances of images as well as the shift and rotation variances of segments in images

A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.Ingeniería, Industria y Construcció

Fuzzy clustering: insights and a new approach

Fuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster. The standard approach to fuzzy clustering introduces the so-called fuzzifier which controls how much clusters may overlap. In this paper we illustrate, how this fuzzifier can help to reduce the number of undesired local minima of the objective function that is associated with fuzzy clustering. Apart from this advantage, the fuzzifier has also some drawbacks that are discussed in this paper. A deeper analysis of the fuzzifier concept leads us to a more general approach to fuzzy clustering that can overcome the problems caused by the fuzzifier

Review on Fuzzy Clustering Algorithms

Image segmentation especially fuzzy-based segmentation techniques are widely used due to effective segmentation performance. For this reason, a number of algorithms are proposed in the literature. This paper presents a survey report of different types of classical fuzzy clustering techniques which are available in the literature

PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TIMUR BERDASARKAN KASUS STUNTING BALITA MENGGUNAKAN ALGORITME FUZZY PARTICLE SWARM OPTIMIZATION-FUZZY C-MEANS

Stunting is a condition that describe the presence of chronical malnutrition problem caused by various condition. East Java Province is a region that has the highest percentage of short toddler in Java Island. Moreover, there is high disparity in cross regency/city and the prevalence rate of stunting in the East Java Province is same as national prevalence rate. Meanwhile, Rencana Pembangunan Jangka Menengah Nasional (RPJMN) 2015-2019 sets the target of national prevalence rate of stunting toddler decreasing in 2019. Based on that problem, this research is clustering regency/city in East Java Province based on stunting toddler case. The clustering uses Fuzzy Particle Swarm Optimization-Fuzzy C-Means (FPSO-FCM). From the clustering result, this research obtains 2 cluster which are cluster of low stunting potential region (cluster 1) and high stunting potential region (cluster 2)

Economic Freedom Index Calculation Using FCM

The Index of Economic Freedom is an annual index and ranking created by The Heritage Foundation and The Wall Street Journal in 1995 to measure the degree of economic freedom in the world's nations. There are many kinds of Economic Freedom Indices depending on variables which many institute or company determine for their research. The aim is to predict countries or regions according to economic parameters. In this study, fuzzy clustering algorithm is proposed for economic freedom ındex calculation. By using degree of memberships founded by FCM, Economic Freedom index will be calculated for regions. Results compared with indices calculated by The Heritage Foundation for the year 2013, 2014, 2015 and 2016. It is showed that FCM is an alternative method for index calculating systems

Semi-supervised machine learning techniques for classification of evolving data in pattern recognition

The amount of data recorded and processed over recent years has increased exponentially. To create intelligent systems that can learn from this data, we need to be able to identify patterns hidden in the data itself, learn these pattern and predict future results based on our current observations. If we think about this system in the context of time, the data itself evolves and so does the nature of the classification problem. As more data become available, different classification algorithms are suitable for a particular setting. At the beginning of the learning cycle when we have a limited amount of data, online learning algorithms are more suitable. When truly large amounts of data become available, we need algorithms that can handle large amounts of data that might be only partially labeled as a result of the bottleneck in the learning pipeline from human labeling of the data. An excellent example of evolving data is gesture recognition, and it is present throughout our work. We need a gesture recognition system to work fast and with very few examples at the beginning. Over time, we are able to collect more data and the system can improve. As the system evolves, the user expects it to work better and not to have to become involved when the classifier is unsure about decisions. This latter situation produces additional unlabeled data. Another example of an application is medical classification, where experts’ time is a rare resource and the amount of received and labeled data disproportionately increases over time. Although the process of data evolution is continuous, we identify three main discrete areas of contribution in different scenarios. When the system is very new and not enough data are available, online learning is used to learn after every single example and to capture the knowledge very fast. With increasing amounts of data, offline learning techniques are applicable. Once the amount of data is overwhelming and the teacher cannot provide labels for all the data, we have another setup that combines labeled and unlabeled data. These three setups define our areas of contribution; and our techniques contribute in each of them with applications to pattern recognition scenarios, such as gesture recognition and sketch recognition. An online learning setup significantly restricts the range of techniques that can be used. In our case, the selected baseline technique is the Evolving TS-Fuzzy Model. The semi-supervised aspect we use is a relation between rules created by this model. Specifically, we propose a transductive similarity model that utilizes the relationship between generated rules based on their decisions about a query sample during the inference time. The activation of each of these rules is adjusted according to the transductive similarity, and the new decision is obtained using the adjusted activation. We also propose several new variations to the transductive similarity itself. Once the amount of data increases, we are not limited to the online learning setup, and we can take advantage of the offline learning scenario, which normally performs better than the online one because of the independence of sample ordering and global optimization with respect to all samples. We use generative methods to obtain data outside of the training set. Specifically, we aim to improve the previously mentioned TS Fuzzy Model by incorporating semi-supervised learning in the offline learning setup without unlabeled data. We use the Universum learning approach and have developed a method called UFuzzy. This method relies on artificially generated examples with high uncertainty (Universum set), and it adjusts the cost function of the algorithm to force the decision boundary to be close to the Universum data. We were able to prove the hypothesis behind the design of the UFuzzy classifier that Universum learning can improve the TS Fuzzy Model and have achieved improved performance on more than two dozen datasets and applications. With increasing amounts of data, we use the last scenario, in which the data comprises both labeled data and additional non-labeled data. This setting is one of the most common ones for semi-supervised learning problems. In this part of our work, we aim to improve the widely popular tecjniques of self-training (and its successor help-training) that are both meta-frameworks over regular classifier methods but require probabilistic representation of output, which can be hard to obtain in the case of discriminative classifiers. Therefore, we develop a new algorithm that uses the modified active learning technique Query-by-Committee (QbC) to sample data with high certainty from the unlabeled set and subsequently embed them into the original training set. Our new method allows us to achieve increased performance over both a range of datasets and a range of classifiers. These three works are connected by gradually relaxing the constraints on the learning setting in which we operate. Although our main motivation behind the development was to increase performance in various real-world tasks (gesture recognition, sketch recognition), we formulated our work as general methods in such a way that they can be used outside a specific application setup, the only restriction being that the underlying data evolve over time. Each of these methods can successfully exist on its own. The best setting in which they can be used is a learning problem where the data evolve over time and it is possible to discretize the evolutionary process. Overall, this work represents a significant contribution to the area of both semi-supervised learning and pattern recognition. It presents new state-of-the-art techniques that overperform baseline solutions, and it opens up new possibilities for future research