Automatic Clustering Techniques Research Articles

Building forecasting model for time series based on improvements in establishing fuzzy relationships and clustering algorithm

This research proposes a new forecasting model for time series with important improvements. The first improvement is the use of the variation between two consecutive times as a universal set and dividing it into intervals with an appropriate number using an automatic clustering technique. The second improvement is the establishment of fuzzy relationships between the built intervals and between each element in the series and these intervals. Finally, using the established relationships, a new forecasting rule is created. The model is presented step by step and detailed with numerical examples, and the proofs for algorithm convergence are provided. It outperforms existing models on well-known datasets including the M3 Competition with 3003 series and M4 Competition datasets with 100,000 series. Another important contribution of this study is the establishment of the R procedure to effectively apply the proposed model to real series.

Longitudinal Study on Sustained Attention to Response Task (SART): Clustering Approach for Mobility and Cognitive Decline.

The Sustained Attention to Response Task (SART) is a computer-based go/no-go task to measure neurocognitive function in older adults. However, simplified average features of this complex dataset lead to loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we combine a novel method to visualise individual trial (raw) information obtained from the SART test in a large population-based study of ageing in Ireland and an automatic clustering technique. We employed a thresholding method, based on the individual trial number of mistakes, to identify poorer SART performances and a fuzzy clusters algorithm to partition the dataset into 3 subgroups, based on the evolution of SART performance after 4 years. Raw SART data were available for 3468 participants aged 50 years and over at baseline. The previously reported SART visualisation-derived feature ‘bad performance’, indicating the number of SART trials with at least 4 mistakes, and its evolution over time, combined with the fuzzy c-mean (FCM) algorithm, individuated 3 clusters corresponding to 3 degrees of physiological dysregulation. The biggest cluster (94% of the cohort) was constituted by healthy participants, a smaller cluster (5% of the cohort) by participants who showed improvement in cognitive and psychological status, and the smallest cluster (1% of the cohort) by participants whose mobility and cognitive functions dramatically declined after 4 years. We were able to identify in a cohort of relatively high-functioning community-dwelling adults a very small group of participants who showed clinically significant decline. The selected smallest subset manifested not only mobility deterioration, but also cognitive decline, the latter being usually hard to detect in population-based studies. The employed techniques could identify at-risk participants with more specificity than current methods, and help clinicians better identify and manage the small proportion of community-dwelling older adults who are at significant risk of functional decline and loss of independence.

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we have also proposed a binary encoding scheme for the particle representation to alleviate this problem. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has also been compared to multiple classical and new clustering solutions and two other automatic clustering techniques in continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion in most datasets. Moreover, the application of the proposed framework to the correlated binary dataset is also reported as a case study. The presence of correlation in a dataset results from the similarity between data points in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. Simplicity, customizability, and flexibility in adding extra conditions to the proposed solution and having a dynamic number of clusters are the advantages of the proposed framework.

Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been proposed and implemented, most of which are able to find good quality clustering results. However, the majority of the traditional clustering algorithms, such as the K-means, K-medoids, and Chameleon, still depend on being provided a priori with the number of clusters and may struggle to deal with problems where the number of clusters is unknown. This lack of vital information may impose some additional computational burdens or requirements on the relevant clustering algorithms. In real-world data clustering analysis problems, the number of clusters in data objects cannot easily be preidentified and so determining the optimal amount of clusters for a dataset of high density and dimensionality is quite a difficult task. Therefore, sophisticated automatic clustering techniques are indispensable because of their flexibility and effectiveness. This paper presents a systematic taxonomical overview and bibliometric analysis of the trends and progress in nature-inspired metaheuristic clustering approaches from the early attempts in the 1990s until today’s novel solutions. Finally, key issues with the formulation of metaheuristic algorithms as a clustering problem and major application areas are also covered in this paper.

An automatic clustering technique for query plan recommendation

The query optimizer is responsible for identifying the most efficient Query Execution Plans (QEP’s). The distributed database relations may be kept in several places. These results in a dramatic increase in the number of alternative query’ plans. The query optimizer cannot exhaustively explore the alternative query plans in a vast search space at reasonable computational costs. Henceforth, reusing the previously generated plans instead of generating new plans for new queries is an efficient technique for query processing. To improve the accuracy of clustering, we’ve rewritten the queries to standardize their structures. Furthermore, TF representation schema has been used to convert the queries into vectors. In this paper, we’ve introduced a multi-objective automatic query plan recommendation method, a combination of incremental DBSCAN and NSGA-II. The quality of the results of incremental DBSCAN has been influenced by Minpts (minimum points) and Eps (epsilon). Two cluster validity indices, Dunn index and Davies–Bouldin index, have simultaneously been optimized to calculate the goodness of an answer. Comparative results have been shown against the incremental DBSCAN and K-means regarding an external cluster validity index, namely, the ARI. By comparing different types of query workloads, we’ve found that the introduced method outperforms the other well-known approaches.

Developing a Taxonomy for Software Engineering Education Through an Empirical Approach

Background: Software engineering has been formalized to a greater extent since 2000; this can be seen in the development of different bodies of knowledge. Objective: This work aims to contribute to organizing software engineering education knowledge, a sub-area in which formalization is still necessary.Method: We propose a process for the construction of controlled vocabularies. We instantiated this process twice; first, using automatic clustering techniques to analyze over 1,000 articles; and then, we focused on concepts related to teaching techniques and methods.Findings: We present a taxonomy with 60 terms organized in three facets at its highest level. The `teaching approaches and methods' category covers 26 terms with their definitions and most relevant references.Implications: The taxonomy can be used by teachers and researchers to understand the breadth of the field, to place their research initiatives in a broader context and to conduct more rigorous searches in the literature. We believe it is necessary to continue working on the taxonomy's expansion and also to carry out validation activities, if possible, including experts' validation.

Index group optimization based on automatic clustering using K-Means genetic algorithm

E-prints UMSIDA is a repository of student and lecturer publication documents at the Muhammadiyah University of Sidoarjo (UMSIDA). The collection of documents is still random and the search can only detect from the title keywords. The increasing culture of writing and research makes it possible for more and more documents as literature. Documents in E-prints are grouped by subject provided by the repository manager and grouped by the admin who uploaded the document. Automatic document grouping can be done by grouping documents based on the contents of the document using the Information Retrieval (IR) approach. The retrieval process is carried out by document processing with tokenisation to obtain data tokens, the data tokens are processed through a stemming process to obtain the stem value of each word. The stem value is processed using the indexing process and word stem to get sentence indexes through the weighting process. The index results stored in the database become document variables that are the features or characteristics of each document. The index of all documents is grouped through Automatic Clustering technique using K-Means Genetic Algorithm.

Sustainable automatic data clustering using hybrid PSO algorithm with mutation

Widespread use of various mobiles, social networks and IOT devices results into continuous generation of the data, often leading to the formation of the big data. Sustainable grouping of such data into various clusters is an open research problem, which aims to provide solutions which are computationally efficient and maintainable over dynamic data. This paper proposes a new sustainable clustering algorithm HPSOM by hybridization of PSO with mutation operator for clustering of the data generated from different networks. The data generated by such networks is usually dynamic and heterogeneous in nature and the number of clusters is not fixed/ known in advance. Hence the proposed algorithm is further extended as AHPSOM for generating and re-adjusting the clusters automatically over the mobile network devices, and it facilitates the generation of sustainable clusters. Firstly, the performance of basic HPSOM is evaluated on six real life data sets and is also compared with some known evolutionary clustering techniques in terms of SWCD and Convergence speed. Then the performance of AHPSOM is evaluated using some synthetic and real life datasets using some validity metrics (cluster numbers, intra cluster distance, inter cluster distance, ARI and F-measure) and is also compared with some prevalent state-of-art automatic clustering techniques. The results show that the proposed algorithm is very efficient in terms of creating well separated, compact and sustainable clusters.

International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization

The effective prediction of pavement performance trends can help in achieving the cost-effective management of pavements over their service life. The international roughness index (IRI) is a widely used pavement performance index, which can be considered as a time-dependent variable in terms of scientific modeling. This research aims to develop an innovative IRI prediction model based on fuzzy-trend time-series forecasting and particle swarm optimization (PSO) techniques. Raw datasets extracted from the Long-Term Pavement Performance database are used for model training, testing, and performance assessment. First, IRI values are divided into different granular spaces, which are considered as the principal factor and subfactors. In addition, the multifactor interval division method is proposed according to the principle of the automatic clustering technique. Next, a second-order fuzzy-trend model and fuzzy-trend relationship classification method are proposed to predict the fuzzy-trend of each factor. Then, the fuzzy-trend states for multiple granular spaces are generated while giving full consideration to various uncertainties. Finally, the PSO technique is used to optimize the performance model while carrying out future IRI forecasting. Comparative experiments are performed using more than 20,000 data items from different regions to verify the effectiveness of the proposed method. The experimental results indicate that the proposed method outperforms other approaches including the polynomial fitting, autoregressive integrated moving average, and backpropagation neural network methods in terms of the root mean squared error (0.191) and relative error (6.37%).

Enhancing point symmetry-based distance for data clustering

In this paper, at first a new point symmetry-based similarity measurement is proposed which satisfies the closure and the symmetry properties of any distance function. The different desirable properties of the new distance are elaborately explained. Thereafter a new clustering algorithm based on the search capability of genetic algorithm is developed where the newly developed point symmetry-based distance is used for cluster assignment. The allocation of points to different clusters is performed in such a way that the closure property is satisfied. The proposed GA with newly developed point symmetry distance based (GAnPS) clustering algorithm is capable of determining different symmetrical shaped clusters having any sizes or convexities. The effectiveness of the proposed GAnPS clustering technique in identifying the proper partitioning is shown for twenty-one data sets having various characteristics. Performance of GAnPS is compared with existing symmetry-based genetic clustering technique, GAPS, three popular and well-known clustering techniques, K-means, expectation maximization and average linkage algorithm. In a part of the paper, the utility of the proposed clustering technique is shown for partitioning a remote sensing satellite image. The last part of the paper deals with the development of some automatic clustering techniques using the newly proposed symmetry-based distance.

Exploring differential evolution and particle swarm optimization to develop some symmetry-based automatic clustering techniques: application to gene clustering

In the current paper, we have developed two bio-inspired fuzzy clustering algorithms by incorporating the optimization techniques, namely differential evolution and particle swarm optimization. Both these clustering techniques can detect symmetrical-shaped clusters utilizing the established point symmetry-based distance measure. Both the proposed approaches are automatic in nature and can detect the number of clusters automatically from a given dataset. A symmetry-based cluster validity measure, F-Sym-index, is used as the objective function to be optimized in order to automatically determine the correct partitioning by both the approaches. The effectiveness of the proposed approaches is shown for automatically clustering some artificial and real-life datasets as well as for clustering some real-life gene expression datasets. The current paper presents a comparative analysis of some meta-heuristic-based clustering approaches, namely newly proposed two techniques and the already existing automatic genetic clustering techniques, VGAPS, GCUK, HNGA. The obtained results are compared with respect to some external cluster validity indices. Moreover, some statistical significance tests, as well as biological significance tests, are also conducted. Finally, results on gene expression datasets have been visualized by using some visualization tools, namely Eisen plot and cluster profile plot.

Automatic data clustering using continuous action-set learning automata and its application in segmentation of images

Most of the proposed algorithms to solve the dynamic clustering problem are based on nature inspired meta-heuristic algorithms. In this paper a different reinforcement based optimization approach called continuous action-set learning automata (CALA) is used and a novel dynamic clustering approach called ACCALA is proposed. CALA is an optimization tool interacting with a random environment and learn the optimal action from the environment feedbacks. In this paper the dynamic clustering problem considered as a noisy optimization problem and the team of CALAs is used to solve this noisy optimization problem. To build such a team of CALAs this paper proposed a new representation of CALAs. Each automaton in this team uses its continuous action-set and defining a suitable action-set for each automaton has a great impact on the CALAs search behavior. In this paper we used the statistical property of data-sets and proposed a new method to automatically find an action-set for each automaton. The performance of ACCALA is evaluated and the results are compared with seven well-known automatic clustering techniques. Also ACCALA is used to perform automatic segmentation. The experimental results are promising and show that the proposed algorithm produced compact and well-separated clusters.

Towards a controlled vocabulary on software engineering education

ABSTRACTSoftware engineering is the discipline that develops all the aspects of the production of software. Although there are guidelines about what topics to include in a software engineering curricula, it is usually unclear which are the best methods to teach them. In any science discipline the construction of a classification schema is a common approach to understand a thematic area. This study examines previous publications in software engineering education to obtain a first controlled vocabulary (a more formal definition of a classification schema) in the field. Publications from 1988 to 2014 were collected and processed using automatic clustering techniques and the outcomes were analysed manually. The result is an initial controlled vocabulary with a taxonomy form with 43 concepts that were identified as the most used in the research publications. We present the classification of the concepts in three facets: ‘what to teach’, ‘how to teach’ and ‘where to teach’ and the evolution of concepts over time.

Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation

AbstractIn this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.

Impacts of synoptic circulation patterns on wind power ramp events in East Japan

This study presents an application of self-organizing maps (SOM) for the climatological/meteorological study of wind power ramp events. SOM constitutes an automatic data-mining clustering technique, which allows for summarizing of a high-dimensional data space in terms of a set of reference vectors. SOM is applied to analyze and establish the relationship between atmospheric synoptic patterns over Japan and wind power generation. SOM is employed on sea level pressure data derived from the JRA-55 reanalysis over the Tohoku region in Japan, whereby a two-dimensional lattice of weather patterns classified during the 1977–2013 period is obtained. Wind-power ramp events (defined as a 30% change in power in less than 6 h) mainly take place during the winter months in East Japan. Our SOM analysis for weather patterns in boreal winter extracts seven typical patterns that are linked to frequent occurrences of wind ramp events. The result of this study suggests that detailed classification of synoptic circulation patterns can be a useful tool for first-order approximations of both the probability of future wind power generation and its variability. Further research relating weather/climate variability and wind power generation is both necessary and valuable in East Asia.

Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm

In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.

Importance of proximity measures in clustering of cancer and miRNA datasets: proposal of an automated framework

Distance plays an important role in the clustering process for allocating data points to different clusters. Several distance or proximity measures have been developed and reported in the literature to determine dissimilarities between two given points. The choice of distance measure depends on a particular domain as well as different data sets of the same domain. It is important to automatically determine the appropriate distance measure which acts best for a particular data set. In this study we have developed an automatic clustering technique using the search capability of multiobjective optimization which can automatically determine the relevant distance measure and the corresponding partitioning from a given data set. Our proposed automated framework is generic in nature i.e., any number of different distance measures can be incorporated into it. In our work we have used four existing widely used distance measures, i.e., Euclidean, line symmetry, point symmetry and city block distance to be explored for each data set. In order to measure the richness of an obtained partitioning using a particular distance, four cluster validity indices, the Silhouette index, the DB index, the adjusted rand index and classification accuracy are used. A new encoding strategy which can encode the set of cluster centers and the particular distance function is used to represent the problem. The appropriate distance function and the corresponding partitioning are determined using the search capability of a multiobjective optimization based technique. The efficiency of the proposed technique is shown on clustering three microRNA and three microarray gene expression data sets having varying complexities. The results show the usefulness of the proposed automated approach.

KOMPARASI METODE PERAMALAN AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY LOGICAL RELATIONSHIPS DENGAN SINGLE EXPONENTIAL SMOOTHING

Automatic clustering technique and fuzzy logical relationships(ACFLR) is one of the forecasting method that used to predict time series data that can be applied in any data. Several previous studies said that this method has a good accuracy. Therefore, this study aims to compare the ACFLR methods with single exponential smoothing method and apply it to simulation data with uniform distribution. The performance of the method is measured based on MSE and MAPE. The results of the comparison of the methods showed that ACFLR has a higher forecasting accuracy than single exponential smoothing. This is evidenced by the value of MSE and MAPE of ACFLR is lower than single exponential smoothing. Keywords: Fuzzy, Forecasting, Automatic Clustering-Fuzzy Logic Relationships, Single Exponential Smoothing

Simultaneous feature selection and symmetry based clustering using multiobjective framework

In this paper a new framework based on multiobjective optimization (MOO), namely FeaClusMOO, is proposed which is capable of identifying the correct partitioning as well as the most relevant set of features from a data set. A newly developed multiobjective simulated annealing based optimization technique namely archived multiobjective simulated annealing (AMOSA) is used as the background strategy for optimization. Here features and cluster centers are encoded in the form of a string. As the objective functions, two internal cluster validity indices measuring the goodness of the obtained partitioning using Euclidean distance and point symmetry based distance, respectively, and a count on the number of features are utilized. These three objectives are optimized simultaneously using AMOSA in order to detect the appropriate subset of features, appropriate number of clusters as well as the appropriate partitioning. Points are allocated to different clusters using a point symmetry based distance. Mutation changes the feature combination as well as the set of cluster centers. Since AMOSA, like any other MOO technique, provides a set of solutions on the final Pareto front, a technique based on the concept of semi-supervised classification is developed to select a solution from the given set. The effectiveness of the proposed FeaClustMOO in comparison with other clustering techniques like its Euclidean distance based version where Euclidean distance is used for cluster assignment, a genetic algorithm based automatic clustering technique (VGAPS-clustering) using point symmetry based distance with all the features, K-means clustering technique with all features is shown for seven higher dimensional data sets obtained from real-life.

Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS(M, N)based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parametersMandN, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.