We establish theoretical limits on the performance of cer-tain data mining algorithms based only on the properties of the data sets being considered. We demonstrate the use of the bounds with an example based on data generated by an artificial world simulator. We point to extensions of this work and to connections with other fields. Introduction1 Data mining techniques can discover and extract hidden patterns about terrorist activities buried in large data stores, or so it is conjectured. However, given the finan-cial and social costs of collecting and processing suc
Agent Based Modeling is the most interesting and advanced approach for simulating a complex system: ...
Data mining is applied in business to find new market opportunities from data stored in operational...
The following thesis explores the impact of the dataset distributional prop- erties on classificatio...
Most of the complexity of common data mining tasks is due to the unknown amount of information conta...
Traditionally, the performance of algorithms is evaluated using worst-case analysis. For a number of...
As more data mining algorithms become available, the answer to one question becomes increasingly imp...
Data mining is the process of finding useful patterns in large sets of data. These algorithms and te...
Granular Computing is not only a computing model for computer centered problem solving, but also a t...
Abstract: Natural computing elements are presented. Data mining algorithms are discussed and quality...
International audienceComputer science is essentially an applied or engineering science , creating t...
Prediction is widely researched area in data mining domain due to its applications. There are many t...
In today’s world,enormous amount of data is available in every field including science, industry, bu...
This review paper presents a shortcoming associated to data mining algorithm(s) classification, clus...
In this chapter, I focus on data-mining and data analytics. It is obvious that without integrated da...
in a database. For many reasons—encoding errors, measurement errors, unrecorded causes of recorded f...
Agent Based Modeling is the most interesting and advanced approach for simulating a complex system: ...
Data mining is applied in business to find new market opportunities from data stored in operational...
The following thesis explores the impact of the dataset distributional prop- erties on classificatio...
Most of the complexity of common data mining tasks is due to the unknown amount of information conta...
Traditionally, the performance of algorithms is evaluated using worst-case analysis. For a number of...
As more data mining algorithms become available, the answer to one question becomes increasingly imp...
Data mining is the process of finding useful patterns in large sets of data. These algorithms and te...
Granular Computing is not only a computing model for computer centered problem solving, but also a t...
Abstract: Natural computing elements are presented. Data mining algorithms are discussed and quality...
International audienceComputer science is essentially an applied or engineering science , creating t...
Prediction is widely researched area in data mining domain due to its applications. There are many t...
In today’s world,enormous amount of data is available in every field including science, industry, bu...
This review paper presents a shortcoming associated to data mining algorithm(s) classification, clus...
In this chapter, I focus on data-mining and data analytics. It is obvious that without integrated da...
in a database. For many reasons—encoding errors, measurement errors, unrecorded causes of recorded f...
Agent Based Modeling is the most interesting and advanced approach for simulating a complex system: ...
Data mining is applied in business to find new market opportunities from data stored in operational...
The following thesis explores the impact of the dataset distributional prop- erties on classificatio...