TY - GEN

T1 - Fusing heterogeneous fuzzy data for clustering

AU - Hathaway, Richard J.

AU - Rogers, G. W.

AU - Bezdek, James C.

AU - Pedrycz, Witold

PY - 1997

Y1 - 1997

N2 - One goal of sensor-fusion methods is the integration of data of various types into a common usable form. Here we seek a uniform framework for the following three types of data: (1) numerical (e.g., x = 74.1); (2) interval (e.g., x = [73.9,75.2]); and (3) fuzzy (e.g., x = tall, where tall is described by a suitable membership function). The problem context of this paper is clustering, which is the problem of separating a set of objects into self-similar groups, but other types of data analysis can be handled similarly. Earlier work on this problem has produced both parametric and nonparametric approaches. The parametric approach is only possible in cases when all the fuzzy data have membership functions coming from a single parametric family of curves, and in that case, the specific parameter values provide numerical data that can easily be used with standard clustering techniques such as the fuzzy c-means algorithm. The more difficult and interesting problem involves the nonparametric case, where there is not a common parametric form for the membership functions. The earlier nonparametric approach produces numerical data for clustering via necessity and possibility values which are derived using a set of `cognitive landmarks'. The main contribution of this note is in presenting a new, simpler nonparametric approach that derives a common usable form of data directly from the membership functions. The new approach is described and then demonstrated using a specific example.

AB - One goal of sensor-fusion methods is the integration of data of various types into a common usable form. Here we seek a uniform framework for the following three types of data: (1) numerical (e.g., x = 74.1); (2) interval (e.g., x = [73.9,75.2]); and (3) fuzzy (e.g., x = tall, where tall is described by a suitable membership function). The problem context of this paper is clustering, which is the problem of separating a set of objects into self-similar groups, but other types of data analysis can be handled similarly. Earlier work on this problem has produced both parametric and nonparametric approaches. The parametric approach is only possible in cases when all the fuzzy data have membership functions coming from a single parametric family of curves, and in that case, the specific parameter values provide numerical data that can easily be used with standard clustering techniques such as the fuzzy c-means algorithm. The more difficult and interesting problem involves the nonparametric case, where there is not a common parametric form for the membership functions. The earlier nonparametric approach produces numerical data for clustering via necessity and possibility values which are derived using a set of `cognitive landmarks'. The main contribution of this note is in presenting a new, simpler nonparametric approach that derives a common usable form of data directly from the membership functions. The new approach is described and then demonstrated using a specific example.

UR - http://www.scopus.com/inward/record.url?scp=0031363452&partnerID=8YFLogxK

U2 - 10.1117/12.280839

DO - 10.1117/12.280839

M3 - Conference article

AN - SCOPUS:0031363452

SN - 0819424838

SN - 9780819424839

T3 - Proceedings of SPIE - The International Society for Optical Engineering

SP - 559

EP - 568

BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Society of Photo-Optical Instrumentation Engineers

T2 - Signal Processing, Sensor Fusion, and Target Recognition VI

Y2 - 21 April 1997 through 24 April 1997

ER -