Data mining research has not only development a large number of algorithms, but also enhanced our knowledge and understanding of their applicability and performance. However, the application of data mining technology in business environments is still no very common, despite the fact that organizations have access to large amounts of data and make decisions that could profit from data mining on a daily basis. One of the reasons is the mismatch between data representation for data storage and data analysis. Data are most commonly stored in multi-table relational databases whereas data mining methods require that the data be represented as a simple feature vector. This work presents a general framework for feature construction from multiple re...
ide powerful modeling component but are often limited to a "flat" file propositional domai...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
Commercial relational databases currently store vast amounts of real-world data. The data within the...
Abstract. In traditional classification setting, training data are represented as a single table, wh...
Various features come from relational data often used to enhance the prediction of statistical model...
One fundamental limitation of classical statistical modeling is the assumption that data is represen...
A new approach is needed to handle huge dataset stored in multiple tables in a very-large database. ...
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption tha...
Abstract. In this paper we propose a novel (multi-)relational classification framework based on prop...
Feature construction through aggregation plays an essential role in modeling relational domains with...
Due to interest in social and economic networks, relational modeling is attracting increasing attent...
Many data sets routinely captured by organizations are relational in nature— from marketing and sale...
This book provides two general granular computing approaches to mining relational data, the first of...
We use clustering to derive new relations which augment database schema used in automatic generation...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
ide powerful modeling component but are often limited to a "flat" file propositional domai...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
Commercial relational databases currently store vast amounts of real-world data. The data within the...
Abstract. In traditional classification setting, training data are represented as a single table, wh...
Various features come from relational data often used to enhance the prediction of statistical model...
One fundamental limitation of classical statistical modeling is the assumption that data is represen...
A new approach is needed to handle huge dataset stored in multiple tables in a very-large database. ...
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption tha...
Abstract. In this paper we propose a novel (multi-)relational classification framework based on prop...
Feature construction through aggregation plays an essential role in modeling relational domains with...
Due to interest in social and economic networks, relational modeling is attracting increasing attent...
Many data sets routinely captured by organizations are relational in nature— from marketing and sale...
This book provides two general granular computing approaches to mining relational data, the first of...
We use clustering to derive new relations which augment database schema used in automatic generation...
summary:Systems aiming at discovering interesting knowledge in data, now commonly called data mining...
ide powerful modeling component but are often limited to a "flat" file propositional domai...
An important aspect of data mining algorithms and systems is that they should scale well to large da...
Commercial relational databases currently store vast amounts of real-world data. The data within the...