A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Modeling temporal functions with granular regression and fuzzy rules
Fuzzy Sets and Systems - Information processing
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Toward Human-Level Machine Intelligence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Multiple regression with fuzzy data
Fuzzy Sets and Systems
A Ten-year Review of Granular Computing
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Information Sciences: an International Journal
Handbook of Granular Computing
Handbook of Granular Computing
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Design of fuzzy rule-based classifiers with semantic cointension
Information Sciences: an International Journal
Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift
IEEE Computational Intelligence Magazine
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Granular computing has gained increasing attention in the last decade. It is motivated by the needs for simply and robust low cost solutions in many real life applications. Addressing these needs, the main objective of granular computing is to develop efficient algorithms. Today granular computing provides a rich variety of such algorithms including methods derived from interval mathematics, fuzzy and rough sets and others. Within this framework granular box regression was proposed recently. Granular box regression uses hyper-dimensional interval numbers to establish a f.g-generalization of a function between several independent variables and one dependent variable. Since granular box regression utilizes intervals a challenge is the detection of outliers. In this paper, we propose three methods tackling outliers in granular box regression and discuss their properties. We also apply these methods to artificial as well as to real data.