The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
Following the fall of the Melaka Sultanate to the Portuguese in 1511, the sultanates of Johor and Aceh emerged as major trading centers alongside Portuguese Melaka. Each power represented wider global interests. Aceh had links with Gujerat, the Ottoman Empire and the Levant. Johor was a center for Javanese merchants and others involved with the Eastern spice trade. Melaka was part of the Estado da India, Portugal's trading empire that extended from Japan to Mozambique. Throughout the sixteenth century, a peculiar balance among the three powers became an important character of the political and economical life in the Straits of Melaka. The arrival of the Dutch in the early seventeenth century upset the balance and led to the decline of Portuguese Melaka. Making extensive use of contemporary Portuguese sources, Paulo Pinto uses geopolitical approach to analyze the financial, political, economic and military institutions that underlay this triangular arrangement, a system that persisted because no one power could achieve an undisputed hegemony. He also considers the position of post-conquest Melaka in the Malay World, where it remained a symbolic center of Malay civilization and a model of Malay political authority despite changes associated with Portuguese rule. In the process provides information on the social, political and genealogical circumstances of the Johor and Aceh sultanates.
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