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Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study. [ABSTRACT FROM AUTHOR]

Inferring the quality of service experienced by wireless users is challenging, as network monitoring does not capture the service perception for each user individually. In this paper, we propose an unsupervised machine learning approach to infer the quality of service experienced by wireless users, based on the different usage profiles of a large-scale wireless network. To this end, our approach correlates the usage data of access points, and the summaries of connection flows passing through the access points in the network. Then, we apply the k-means clustering algorithm to infer different network usage profiles. We evaluate our proposed approach to infer QoS on a real large-scale wireless network, and the results show that discriminating the flows into five clusters allows identifying prevalent usage profiles of the degraded state of the network and overload conditions in access points, considering only the flow summaries. [ABSTRACT FROM AUTHOR]

Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised classification algorithm at its learning phase. In this case, the accuracy of this classification algorithm, when applied to unlabelled data, is likely to suffer considerably. In this paper, we introduce a clustering-based method capable of reducing the number of mislabelled entities in data sets. Our method can be summarised as follows: (i) cluster the data set; (ii) select the entities that have the most potential to be assigned to correct clusters; (iii) use the entities of the previous step to define the core clusters and map them to the labels using a confusion matrix; (iv) use the core clusters and our cluster membership criterion to correct the labels of the remaining entities. We perform numerous experiments to validate our method empirically using k-nearest neighbour classifiers as a benchmark. We experiment with both synthetic and real-world data sets with different proportions of mislabelled entities. Our experiments demonstrate that the proposed method produces promising results. Thus, it could be used as a preprocessing data correction step of a supervised machine learning algorithm. [ABSTRACT FROM AUTHOR]

The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained by k-means algorithm. In this paper, we modified the global k-means algorithm to eliminate the singleton clusters at first, and then we apply MinMax k-means clustering error method to global k-means algorithm to overcome the effect of bad initialization, proposed the global Minmax k-means algorithm. The proposed clustering method is tested on some popular data sets and compared to the k-means algorithm, the global k-means algorithm and the MinMax k-means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper. [ABSTRACT FROM AUTHOR]

Because of its deterministic nature, K-means does not yield confidence information about centroids and estimated cluster memberships, although this could be useful for inferential purposes. In this paper we propose to arrive at such information by means of a non-parametric bootstrap procedure, the performance of which is tested in an extensive simulation study. Results show that the coverage of hyper-ellipsoid bootstrap confidence regions for the centroids is in general close to the nominal coverage probability. For the cluster memberships, we found that probabilistic membership information derived from the bootstrap analysis can be used to improve the cluster assignment of individual objects, albeit only in the case of a very large number of clusters. However, in the case of smaller numbers of clusters, the probabilistic membership information still appeared to be useful as it indicates for which objects the cluster assignment resulting from the analysis of the original data is likely to be correct; hence, this information can be used to construct a partial clustering in which the latter objects only are assigned to clusters. [ABSTRACT FROM AUTHOR]

Data obtained from additive manufactured components can be analyzed to gain a better understanding of the manufacturing physics and to improve the quality of the parts. However, additive manufacturing is a complex process with many sensitivities. Machine learning has recently been used in additive manufacturing to model and evaluate processes. It is not possible to provide a single regression model to predict mechanical behavior as the properties will vary based on the component geometry and in regions within a component. To provide a separate regression model for each region, it is better to categorize several regions for multiple geometries by their post- fabrication properties (maximum and minimum principal stresses and distortion). Clustering is a method for analyzing the quality of parts with similar characteristics in diverse areas. Twenty-three distinct geometries with numerous geometric characteristics, each experiencing a different history of heat during fabrication (due to their thickness and material distribution), are analyzed in this study. Three different clustering methods are employed (self-organizing map, k-means clustering, and fuzzy c-means clustering). The results are presented in two parts. For the first case, a localized approach is taken, where a comprehensive data set is utilized. The observed maximum coefficient of variance is 0.07969. For the second case, each shape is considered as an instance (sample) with general geometric characteristics. Similar trends between different clustering methods were extracted for the global approach, highlighting the potential of this method, but the clustering results are dependent on the clustering method. According to these results, the analysis of the local data provides a deeper understanding of post-fabrication properties clustering. Additional geometric-based characteristics will be developed to refine and improve the global approach. [ABSTRACT FROM AUTHOR]

Background: We are living in an age where data is everywhere and grows up in a very speedy way. Thanks to sensors, mobile phones and social networks, we can gather a hug amount of information to understand human behavior as well as his individual life. In healthcare system, big data analytics and machine learning algorithms prove their effectiveness and efficiency in saving lives and predicting new diseases. This triggered the idea of taking advantages of those tools and algorithms to create systems that involve both doctors and patients in the treatment of disease, predict outcomes and use real-time risk factors from sensors and mobile phones. Methods: We distinguish three types of data: data from sensors, data from mobile phones and data registered or updated by the patient in a mobile app we created. We take advantages from IoT systems such as Raspberry Pi to collect and process data coming from sensors. All data collected is sent to a NoSql Server to be then analyzed and processed in Databricks Spark. K-means centroid clustering algorithms is used to build the predictive model, create partitions and make predictions. To validate results in term of efficiency and effectiveness, we used clustering validations techniques: Random K, Silhouette and Elbow methods. Results: The main contribution of our work is the implementation of a new system that has the capability to be applied in several prediction disease researches using Big Data Analytics and IoT. Also, comparing to other studies in literature that use only medical or maternal risk factors from echography; our work had the advantage to use real-time risk factors (maternal and medical) gathered from sensors, react in advance and track diseases. As a case study, we create an e-monitoring real-time miscarriage prediction system to save baby's lives and help pregnant women. In fact, doctors receive the results of clustering and track theirs patient through our mobile app to react in term of miscarriage to avoid non-suitable outcomes. While pregnant women receive only advices based on their behaviors. The system uses 15 real-time risk factors and our dataset contains more than 1,000,000 JSON files. Elbow method affirm three as the optimal number of clusters and we reach 0.99 as a value of Silhouette method, which is a good sign that clusters are well separated and matched. [ABSTRACT FROM AUTHOR]

The k-means algorithm is widely used for clustering, compressing, and summarizing vector data. We present a fast and memory-efficient GPU-based algorithm for exact k-means, Asynchronous Selective Batched K-means (ASB K-means). Unlike most GPU-based k-means algorithms that require loading the whole dataset onto the GPU for clustering, the amount of GPU memory required to run our algorithm can be chosen to be much smaller than the size of the whole dataset. Thus, our algorithm can cluster datasets whose size exceeds the available GPU memory. The algorithm works in a batched fashion and applies the triangle inequality in each k-means iteration to omit a data point if its membership assignment, i.e., the cluster it belongs to, remains unchanged, thus significantly reducing the number of data points that need to be transferred between the CPU's RAM and the GPU's global memory and enabling the algorithm to very efficiently process large datasets. Our algorithm can be substantially faster than a GPU-based implementation of standard k-means even in situations when application of the standard algorithm is feasible because the whole dataset fits into GPU memory. Experiments show that ASB K-means can run up to 15x times faster than a standard GPU-based implementation of k-means, and it also outperforms the GPU-based k-means implementation in NVIDIA's open-source RAPIDS machine learning library on all the datasets used in our experiments. [ABSTRACT FROM AUTHOR]

Particle classification is essential for geotechnical engineering practice since particle shapes correlate with the mechanical and hydraulic properties of sand layers. Traditional shape classification is tedious, subjective, and time-consuming because it depends on manual visual comparison with reference particles. This study demonstrates the feasibility of employing machine learning algorithms for sand classification. Machine learning (ML) models are increasingly being introduced for automatic identification and classification of various objects. Nine types of sand were selected, and the analysis was based on 2000 binary images of each sand that were obtained from dynamic image analysis (DIA). Each particle was represented by six engineering size and four shape descriptors. The efficacy of seven ML models for automatically classifying individual sand particles was explored. The study demonstrates that the size and shape descriptors are efficient and robust to identify up to 75% of sand particles, using a neural network classifier. In addition, use of scale-invariant feature transform (SIFT) features was also explored to permit future generalization of sand classification using image datasets containing images with different scales and resolutions. Adding SIFT to size and shape can increase classification accuracy to 83% using a random forest classifier. The analysis also reveals that histograms of orientation gradients of SIFT keypoints in sand appear well correlated with sphericity and convexity of particles. This study suggests that a dataset of 2000 particles per sand is sufficient for optimal classification performance and that image preprocessing of DIA images was not necessary. [ABSTRACT FROM AUTHOR]

Current clustering algorithms solved a few of the issues around clustering such as similarity measure learning, or the cluster number estimation. For instance, some clustering algorithms can learn the data similarity matrix, but to do so they need to know the cluster number beforehand. On the other hand, some clustering algorithms estimate the cluster number, but to do so they need the similarity matrix as an input. Real-world data often contains redundant features and outliers, which many algorithms are susceptive to. None of the current clustering algorithms are able to learn the data similarity measure and the cluster number simultaneously, and at the same time reduce the influence of outliers and redundant features. Here we propose a joint feature selection with dynamic spectral clustering (FSDS) algorithm that not only learns the cluster number k and data similarity measure simultaneously, but also employs the L 2 , 1 -norm to reduce the influence of outliers and redundant features. The optimal performance could be reached when all the separated stages are combined in a unified way. Experimental results on eight real-world benchmark datasets show that our FSDS clustering algorithm outperformed the comparison clustering algorithms in terms of two evaluation metrics for clustering algorithms including ACC and Purity. [ABSTRACT FROM AUTHOR]

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques. [ABSTRACT FROM AUTHOR]