Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...

A survey on pre-processing techniques: relevant issues in the context of environmental data mining

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.Peer ReviewedPostprint (author's final draft

Adaptive Capacity of the Water Management Systems of Two Medieval Khmer Cities, Angkor and Koh Ker

abstract: Understanding the resilience of water management systems is critical for the continued existence and growth of communities today, in urban and rural contexts alike. In recent years, many studies have evaluated long-term human-environmental interactions related to water management across the world, highlighting both resilient systems and those that eventually succumb to their vulnerabilities. To understand the multitude of factors impacting resilience, scholars often use the concept of adaptive capacity. Adaptive capacity is the ability of actors in a system to make adaptations in anticipation of and in response to change to minimize potential negative impacts. In this three-paper dissertation, I evaluate the adaptive capacity of the water management systems of two medieval Khmer cities, located in present-day Cambodia, over the course of centuries. Angkor was the capital of the Khmer Empire for over 600 years (9 th -15 th centuries CE), except for one brief period when the capital was relocated to Koh Ker (921 – 944 CE). These cities both have massive water management systems that provide a comparative context for studying resilience; while Angkor thrived for hundreds of years, Koh Ker was occupied as the capital of the empire for a relatively short period. In the first paper, I trace the chronological and spatial development of two types of settlement patterns (epicenters and lower-density temple-reservoir settlement units) at Angkor in relation to state-sponsored hydraulic infrastructure. In the second and third papers, I conduct a diachronic analysis using empirical data for the adaptive capacity of the water management systems at both cities. The results suggest that adaptive capacity is useful for identifying causal factors in the resilience and failures of systems over the long term. The case studies also demonstrate the importance and warn of the danger of large centralized water management features.Dissertation/ThesisDoctoral Dissertation Anthropology 201

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p