A Fuzzy Measure Similarity Between Sets of Linguistic Summaries

摘要: In this paper, we consider the problem of evaluating similarity two sets linguistic summaries sensor data. Huge amounts available data cause a dramatic need for summarization. continuous monitoring, it is useful to compare one time interval with another, example, detect anomalies or predict onset change from normal state. Assuming that capture essence data, sufficient only those summaries, i.e., they are descriptive features recognition. previous work, developed measure between individual and proved associated dissimilarity metric. Additionally, proposed some basic methods combine these similarities into an aggregate value. Here, develop novel parameter free method, which based on fuzzy measures integrals, fuse will produce closeness measurement summaries. We provide case study eldercare domain where goal different nighttime patterns detection. The reasons studying twofold: First, natural communication tool health care providers in decision support system, second, due extremely large volume raw create compact automated reasoning detection prediction changes as part system.