The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data...
The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge di...
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g., machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its...
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The ...
Feature selection is an essential step for successful data mining applications and has practical significance in many areas, such as statistics, pattern recognition, machine learning, and knowledge discovery. Through a clear, concise, and coherent presentation of topics, Computational Methods of Feature Selection systematically covers the key concepts, representative approaches, and inventive applications of various aspects of feature selection. The book bridges the widening gap between existing texts and rapid developments in the field by presenting recent research works from...
Feature selection is an essential step for successful data mining applications and has practical significance in many areas, such as statistics, pa...
This book constitutes the refereed proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence, PRICAI'98, held in Singapore, in November 1998. The 56 revised papers presented were carefully reviewed and selected from a total of 197 submissions received from 25 countries. The papers are organized in sections on induction; multi-agent architecture; knowledge acquisition, modeling and validation; reasoning; knowledge discovery and data mining; knowledge management; application of fuzzy logic; applications of neural networks; searching; Bayesian networks; text...
This book constitutes the refereed proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence, PRICAI'98, held in Singapor...
This volume contains the papers presented at the 8th International Conference onDiscoveryScience(DS2005)heldinSingapore, RepublicofSingapore, during the days from 8 11 of October 2005. The main objective of the Discovery Science (DS) conference series is to p- vide an open forum for intensive discussions and the exchange of new ideas and information among researchers working in the area of automating scienti?c d- covery or working on tools for supporting the human process of discovery in science. It has been a successful arrangement in the past to co-locate the DS conference with the...
This volume contains the papers presented at the 8th International Conference onDiscoveryScience(DS2005)heldinSingapore, RepublicofSingapore, during t...
The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data...
The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge di...
There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about...
There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining...
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com- puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g., machine learning and data mining sys- tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its...
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The ...
