This paper presents the theory and application of a high speed, low cost pattern classifier. The proposed classifier is built around a special class of sparse network referred to as Cellular Automata ...(CA). A specific class of CA, termed as Multiple Attractor Cellular Automata (MACA), has been evolved through Genetic Algorithm (GA) formulation to perform the task of pattern clas-sification. The versatility of the classification scheme is illustrated through its application in three diverse fields - data mining, image compression, and fault diagnosis. Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Hardware architecture of the proposed classifier has been also reported.
This study is aimed at determining the future share net inflows and outflows by using the characteristics of Exchange Traded Funds (ETF) as variables in a data mining based analytic methodology. The ...relationship between net flows is closely related to investor perception of the future and past performance of mutual funds. In order to explore the relationship between investor's perception of ETFs and subsequent net flows, this study is designed to shed light on the multifaceted linkages between fund characteristics and net flows. An international selection of 222 ETFs from one of the top three ETF providers is used in this study, of which fifteen attributes from each fund are used because they are likely to be contributors to fund inflows and outflows. Cross-Industry Standard Process for Data Mining (CRISP-DM) is used in this study accompanied with machine learning tools to develop a neural network which will forecast a positive or negative flow of net assets for ETFs.
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled 'DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, ...Association and Classification' we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It ...is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. Our model is the expansion of a combination of two mathematical models: a reaction-diffusion system provides a framework for modeling the flow of influences between local area groups and an ecological system models the latent interactions between each query. Also, by leveraging the concept of physics-informed neural networks, FluxCube achieves high interpretability obtained from the parameters and high forecasting performance, together. Extensive experiments on real datasets showed that FluxCube outperforms comparable models in terms of the forecasting accuracy, and each component in FluxCube contributes to the enhanced performance. We then show some case studies that FluxCube can extract useful latent interactions between queries and area groups.
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled "DATA MINING: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, ...Social, Biological and other Applications" we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
With the application and popularization of Internet, the data of network resources are becoming more and more massive and diversified. However, the rapid growth of information conflicts with the ...backwardness of data mining technologies. Under the premise of existing mass data mining technologies, people need to carry out innovative research and decision-making management. As a result, the distributed data mining based on the cloud computing architecture came into our life. It is very effective to make up for the shortcomings and solve the related problems encountered in the distributed data mining. I believe that the application of distributed data mining based on the cloud computing architecture is Promising.
The growing use of Internet in everyday life has been creating new challenges and opportunities to use data mining techniques. A relatively new trend in the Internet is the deep web. As a large ...number of deep web data sources tend to provide similar data, an important problem is to perform offline analysis to understand the differences in data available from different sources. This paper introduces data mining methods to extract a high-level summary of the differences in data provided by different deep web data sources. We consider pattern of values with respect to the same entity and we formulate a new data mining problem, which we refer to as differential rule mining. We have developed an algorithm for mining such rules. Our method includes a pruning method to summarize the identified differential rules. For efficiency, a hash-table is used to accelerate the pruning process. We show the effectiveness, efficiency, and utility of our methods by analyzing data across four travel-related web-sites.
The new way of sharing, communicating, storing, personal assisting, and even purchasing products or services, gender millions of data. In any application domain, this data will process and converted ...into information. The conversion implies the complementary relationship between several disciplines. A successful example occurs between the Internet of Things (IoT), Computational Intelligence (CI), Data Mining (DM), and Big Data (BD). Globally, various studies cases combined IoT, CI, DM, and BD. So, some sectors that provide services have a more advanced interaction, for example, the health with monitoring and detection, diagnostic and care technologies. In other service sectors, however, technological advances have not evolved at the same rate. A specific case in Argentina is long-distance land transport. This paper presents a plan and guide, combining different devices and technologies to reactivate this type of organization. We propose the integration of IoT, CI, Business Intelligence (BI), and Business Analytics (BA). Also, we experiment, in the transportation sector, with different algorithms and software to analyze images and data. Finally, we get conclusions about the proposed strategy. Keywords: Business Analytics, Business Intelligence, Computational Intelligence, Internet of Things, Transport. La nueva forma de compartir, comunicar, almacenar, de asistencia personal e incluso de comprar productos o servicios, genera millones de datos. En cualquier dominio de aplicacion, estos datos se procesaran y convertiran en informacion. La conversion implica la relacion complementaria entre varias disciplinas. Un ejemplo exitoso ocurre entre Internet de las Cosas (IoT), Inteligencia Computacional (CI), Mineria de Datos (DM) y Big Data (BD). A nivel mundial, varios casos de estudio combinaron IoT, CI, DM y BD. Asi, algunos sectores que brindan servicios tienen una interaccion mas avanzada, por ejemplo, la salud con las tecnologias de monitoreo y deteccion, diagnostico y atencion. En otros sectores de servicios, sin embargo, los avances tecnologicos no han evolucionado al mismo ritmo. Un caso especifico en Argentina es el transporte terrestre de larga distancia. Este articulo presenta un plan y una guia, combinando diferentes dispositivos y tecnologias para reactivar este tipo de organizaciones. Proponemos la integracion de IoT, CI, Inteligencia de Negocio (BI) y Analitica de Negocio (BA). Ademas, experimentamos en el sector del transporte, con diferentes algoritmos y software, para analizar imagenes y datos. Finalmente, se obtienen conclusiones sobre la estrategia propuesta. Palabras claves: Analitica de Negocio, Inteligencia de Negocio, Inteligencia Computacional, Internet de las Cosas, Transporte.
En el origen y evolución de las finanzas ha resultado oportuno alinear la teoría con la práctica, con el fin de pro - mover argumentos sólidos. Sin embargo, esta perspectiva está lejos de ser ...aplicada, ya que es común que se empleen los aspectos prácticos sin adherir a su interpretación las hipótesis teóricas sobre la influencia, relación y asociatividad de los términos. En este sentido, este estudio se plantea como objetivo evaluar si el teorema Modigliani-Miller y sus supuestos son aplicables a las pymes pertenecientes al sector comercio del Ecuador. Para ello se usan dos etapas metodológicas. En primer lugar, se utilizan con modelos no supervisados mediante Partitioning Around Medoids, con el fin de clasificar y aumentar la homogeneidad en la interpretación de modelos. Y, en segundo lugar, se emplean modelos de interpretación lineal clasificados con el objeto de explicar la relación subyacente de los diversos indicadores con la estructura de capital. Con las iteraciones se puede concluir que la teoría que mayor adaptabilidad posee en este grupo de empresas es la del trade-off, la cual sugiere que sí existe una estructura adecuada, que resulta de la combinación de factores financieros.
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