Title :
Soft multi-modal data fusion
Author :
Coppock, Sarah ; Mazlack, Lawrence
Author_Institution :
Dept. of Electr. & Comput. Eng. & Comput. Sci., Cincinnati Univ., OH, USA
Abstract :
Clustering groups items together that are most similar to each other and sets those that are least similar into different clusters. Methods have been developed to cluster records in a data set that are of only qualitative or quantitative data. Data sets exist that contain a mix of qualitative (nominal and ordinal) and quantitative (discrete and continuous) data. Clustering records of mixed kinds of data is a difficult problem. A metric to measure the similarity between records of mixed data types is needed. Once a clustering is found, we do not know how to best evaluate the quality of the clustering when there is a mixture of data varieties.
Keywords :
data mining; pattern clustering; sensor fusion; clustering records; data mining; knowledge discovery; mixed data; partitioning; qualitative data; quantitative data; similarity metrics; soft multimodal data fusion; Blood; Clustering methods; Computer science; Credit cards; Data analysis; Data mining; Diseases; Humans; Medical treatment; Sensor fusion;
Conference_Titel :
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
Print_ISBN :
0-7803-7810-5
DOI :
10.1109/FUZZ.2003.1209438
