The proliferation of urban sensing, IoT, and big data in cities provides unprecedented
opportunities for a deeper understanding of occupant behaviour and energy usage patterns …

Traditional data mining research mainly focus] es on developing, demonstrating, and
pushing the use of specific algorithms and models. The process of data mining stops at …

Posting reviews online has become an increasingly popular way for people to express
opinions and sentiments toward the products bought or services received. Analyzing the …

Additive tree models (ATMs) are widely used for data mining and machine learning.
Important examples of ATMs include random forest, adaboost (with decision trees as weak …

Quantitative intelligence based traditional data mining is facing grand challenges from real-
world enterprise and cross-organization applications. For instance, the usual demonstration …

Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying
expected technical interestingness. There are often many patterns mined but business …

Abstract Domain-driven data mining of health care data poses unique challenges. The aim
of this paper is to explore the advantages and the challenges of a 'domain-led …

In privacy-preserving data mining (PPDM), a widely used method for achieving data mining
goals while preserving privacy is based on k-anonymity. This method, which protects subject …

Player performance prediction is a serious problem in every sport since it brings valuable
future information for managers to make important decisions. In baseball industries, there …

Zusammenfassung Die Gewinnung von Wissen aus großen Datenbeständen basiert oftmals
auf der statistischen und teilweise manuellen Analyse und Interpretation. Die Menge an …