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Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multi-dimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.

Data Mining is a method of finding patterns from vast quantities of data and information. The data sources include databases, data centers, the internet, and other data storage forms; or data that is dynamically streaming into the network. Estimation of effort is very important in the cost estimation of a software development project, and very critical in the software life development cycle planning process. This paper offers a description of the latest data mining techniques used in estimating software effort, and these techniques are divided into two, namely: Classical and Modern, based on when they were developed and when they started to be used in business administration. The Classical techniques are the ones that have been in use for decades and are still relevant until today, while the Modern ones are the ones that have been introduced recently and have gained wide acceptance in the system. The Classical techniques are Statistical methods, Nearest Neighbours, Clustering and Regression Analysis, while Neural Networks, Rule Induction Systems and Decision Trees are included in the Modern techniques. This paper offers an overview of these strategies in terms of their features, benefits, drawbacks and use areas. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Data Mining is a method of finding patterns from vast quantities of data and information. The data sources include databases, data centers, the internet, and other data storage forms; or data that is dynamically streaming into the network. Estimation of effort is very important in the cost estimation of a software development project, and very critical in the software life development cycle planning process. This paper offers a description of the latest data mining techniques used in estimating software effort, and these techniques are divided into two, namely: Classical and Modern, based on when they were developed and when they started to be used in business administration. The Classical techniques are the ones that have been in use for decades and are still relevant until today, while the Modern ones are the ones that have been introduced recently and have gained wide acceptance in the system. The Classical techniques are Statistical methods, Nearest Neighbours, Clustering and Regression Analysis, while Neural Networks, Rule Induction Systems and Decision Trees are included in the Modern techniques. This paper offers an overview of these strategies in terms of their features, benefits, drawbacks and use areas. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

A novel data driven approach to batch process monitoring is presented, which combines the k-Nearest Neighbour rule with the dynamic time warping (DTW) distance. This online method (DTW-NN) calculates the DTW distance between an ongoing batch, and each batch in a reference database of batches produced under normal operating conditions (NOC). The sum of the k smallest DTW distances is monitored. If a fault occurs in the ongoing batch, then this distance increases and an alarm is generated. The monitoring statistic is easy to interpret, being a direct measure of similarity of the ongoing batch to its nearest NOC predecessors and the method makes no distributional assumptions regarding normal operating conditions. DTW-NN is applied to four extensive datasets from simulated batch production of penicillin, and tested on a wide variety of fault types, magnitudes and onset times. Performance of DTW-NN is contrasted with a benchmark multiway PCA approach, and DTW-NN is shown to perform particularly well when there is clustering of batches under NOC.

The k-nearest neighbour (kNN) problem appears in many different fields of computer science, such as computer animation and robotics. In crowd simulation, kNN queries are typically used by a collision-avoidance method to prevent unnecessary computations. Many different methods for finding these neighbours exist, but it is unclear which will work best in crowd simulations, an application which is characterised by low dimensionality and frequent change of the data points. We therefore compare several data structures for performing kNN queries. We find that the nanoflann implementation of a k-d tree offers the best performance by far on many different scenarios, processing 100,000 agents in about 35 milliseconds on a fast consumer PC.

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and imple ment the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to elec tricity consumption from a public Spanish university, by using a Spark cluster.

k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging., Los algoritmos de vecinos cercanos (k-NN) son métodos ampliamente empleados en la clasificación estadística. Los cuales destacan por ser precisos y por no depender de ningún supuesto distribucional. A pesar de estas ventajas tienen el inconveniente de implicar un alto costo computacional. Conseguir formas eficientes de implementarlos es un reto impor- tante para el desarrollo del reconocimiento de patrones. En este trabajo se discute una versión mejorada del algoritmo k-NN Condensación Controlada y se analiza su potencial en la identificación de color en tiempo real. Se basa en la representación de datos de entrenamiento en función de un conjunto reducido de prototipos informativos. Incluye dos parámetros que controlan el balance entre rapidez y precisión. Esto permite definir el porcentaje de condensación sin sacrificar demasiado la precisión del algoritmo. Probamos nuestra propuesta en un problema de clasificación instantánea en imágenes de video. Logramos la identificación de color en tiempo real mediante el algoritmo k-NN Condensación Controlada ejecutado con técnicas de programación multihilos. Los resultados obtenidos hasta el momento son alentadores.

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and imple ment the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to elec tricity consumption from a public Spanish university, by using a Spark cluster.

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and imple ment the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to elec tricity consumption from a public Spanish university, by using a Spark cluster.

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and imple ment the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to elec tricity consumption from a public Spanish university, by using a Spark cluster.

k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging., Los algoritmos de vecinos cercanos (k-NN) son métodos ampliamente empleados en la clasificación estadística. Los cuales destacan por ser precisos y por no depender de ningún supuesto distribucional. A pesar de estas ventajas tienen el inconveniente de implicar un alto costo computacional. Conseguir formas eficientes de implementarlos es un reto impor- tante para el desarrollo del reconocimiento de patrones. En este trabajo se discute una versión mejorada del algoritmo k-NN Condensación Controlada y se analiza su potencial en la identificación de color en tiempo real. Se basa en la representación de datos de entrenamiento en función de un conjunto reducido de prototipos informativos. Incluye dos parámetros que controlan el balance entre rapidez y precisión. Esto permite definir el porcentaje de condensación sin sacrificar demasiado la precisión del algoritmo. Probamos nuestra propuesta en un problema de clasificación instantánea en imágenes de video. Logramos la identificación de color en tiempo real mediante el algoritmo k-NN Condensación Controlada ejecutado con técnicas de programación multihilos. Los resultados obtenidos hasta el momento son alentadores.

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and imple ment the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to elec tricity consumption from a public Spanish university, by using a Spark cluster.

Associated with the establishment of removal of water sources in savanna ecosystems is the issue of the effects of such management actions on animal movement and habitat selection, longer term implications on population levels, and impacts of such change on habitat degradation and soil erosion. Extant metrics used to describe the spatial distribution of water sources on the landscape often fall short of providing source-specific information, making them hard to apply in small-scale management settings. Using the Klaserie Private Nature Reserve (KPNR) as a case study, we compare a buffer framework, describing distances to water, a nearest neighbour framework, a spatial location-allocation framework (SLAF) created in a geographic information system (GIS). These three frameworks can be combined into one GIS to demonstrate site-specific information on water source distribution, in addition to system-wide descriptions. The visually accessible quality of a GIS allows qualitative input from managers and property owners to achieve quantifiable management goals. The duality of database and visual representation provides a useful tool to assess the role of individual water sources and can easily be updated to reflect changes in their distribution.

Associated with the establishment of removal of water sources in savanna ecosystems is the issue of the effects of such management actions on animal movement and habitat selection, longer term implications on population levels, and impacts of such change on habitat degradation and soil erosion. Extant metrics used to describe the spatial distribution of water sources on the landscape often fall short of providing source-specific information, making them hard to apply in small-scale management settings. Using the Klaserie Private Nature Reserve (KPNR) as a case study, we compare a buffer framework, describing distances to water, a nearest neighbour framework, a spatial location-allocation framework (SLAF) created in a geographic information system (GIS). These three frameworks can be combined into one GIS to demonstrate site-specific information on water source distribution, in addition to system-wide descriptions. The visually accessible quality of a GIS allows qualitative input from managers and property owners to achieve quantifiable management goals. The duality of database and visual representation provides a useful tool to assess the role of individual water sources and can easily be updated to reflect changes in their distribution.

Associated with the establishment or removal of water sources in savanna ecosystems is the issue of the effects of such management actions on animal movement and habitat selection, longer term implications on population levels, and impacts of such change on habitat degradation and soil erosion. Extant metrics used to describe the spatial distribution of water sources on the landscape often fall short of providing source-specific information, making them hard to apply in small-scale management settings. Using the Klaserie Private Nature Reserve (KPNR) as a case study, we compare a buffer framework, describing distances to water, a nearest neighbour framework, and a spatial location-allocation framework (SLAF) created in a geographic information system (GIS). These three frameworks can be combined into one GIS to demonstrate site-specific information on water source distribution, in addition to system-wide descriptions. The visually accessible quality of a GIS allows qualitative input from managers and property owners to achieve quantifiable management goals. The duality of database and visual representation provides a useful tool to assess the role of individual water sources and can easily be updated to reflect changes in their distribution.

Associated with the establishment or removal of water sources in savanna ecosystems is the issue of the effects of such management actions on animal movement and habitat selection, longer term implications on population levels, and impacts of such change on habitat degradation and soil erosion. Extant metrics used to describe the spatial distribution of water sources on the landscape often fall short of providing source-specific information, making them hard to apply in small-scale management settings. Using the Klaserie Private Nature Reserve (KPNR) as a case study, we compare a buffer framework, describing distances to water, a nearest neighbour framework, and a spatial location-allocation framework (SLAF) created in a geographic information system (GIS). These three frameworks can be combined into one GIS to demonstrate site-specific information on water source distribution, in addition to system-wide descriptions. The visually accessible quality of a GIS allows qualitative input from managers and property owners to achieve quantifiable management goals. The duality of database and visual representation provides a useful tool to assess the role of individual water sources and can easily be updated to reflect changes in their distribution.

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in order to base the algorithm S-NN (Similar Nearest Neighbours). Traditional clustering algorithms are very sensitive to the user-defined parameters and an expert knowledge is required to choose the values. Frequently, these algorithms are fragile in the presence of outliers and any adjust well to spherical shapes. Experiments have shown that S-NN is accurate discovering arbitrary shapes and density clusters, since it takes into account the internal features of each cluster, and it does not depend on a user-supplied static model. S-NN achieve this by collecting the nearest neighbours with the same label until the enemy is found (it has not the same label). The determinism and the results offered to the researcher turn it into a valuable tool for the representation of the inherent knowledge to the labelled databases.

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in order to base the algorithm S-NN (Similar Nearest Neighbours). Traditional clustering algorithms are very sensitive to the user-defined parameters and an expert knowledge is required to choose the values. Frequently, these algorithms are fragile in the presence of outliers and any adjust well to spherical shapes. Experiments have shown that S-NN is accurate discovering arbitrary shapes and density clusters, since it takes into account the internal features of each cluster, and it does not depend on a user-supplied static model. S-NN achieve this by collecting the nearest neighbours with the same label until the enemy is found (it has not the same label). The determinism and the results offered to the researcher turn it into a valuable tool for the representation of the inherent knowledge to the labelled databases.

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in order to base the algorithm S-NN (Similar Nearest Neighbours). Traditional clustering algorithms are very sensitive to the user-defined parameters and an expert knowledge is required to choose the values. Frequently, these algorithms are fragile in the presence of outliers and any adjust well to spherical shapes. Experiments have shown that S-NN is accurate discovering arbitrary shapes and density clusters, since it takes into account the internal features of each cluster, and it does not depend on a user-supplied static model. S-NN achieve this by collecting the nearest neighbours with the same label until the enemy is found (it has not the same label). The determinism and the results offered to the researcher turn it into a valuable tool for the representation of the inherent knowledge to the labelled databases.