A patient diabetes monitoring system for a patient comprising: - a physiological data input device which acquires under unsupervised conditions a plurality of physiological measurements of the patient within a time window to generate at least one time window dataset of collected unsupervised daily monitoring profiles; - a memory storing an unsupervised daily monitoring profile clustering algorithm; and - a processor in communication with said input device to receive said generated at least one time window dataset, and in communication with said memory in order to execute said unsupervised daily monitoring profile clustering algorithm, characterized in that said unsupervised daily monitoring profile clustering algorithm when executed by said processor causes said processor automatically to: - pre-process the at least one time window dataset to control an amount of bias/aggressiveness of a penalty in the collected unsupervised daily monitoring profiles due to the unsupervised conditions via a data transformation of the at least one time window dataset to generate a pre-processed dataset which blunts responses on hyper-side and/or hypo-side of a glucose response spectrum of a time series from the at least one time window dataset, wherein the data transformation makes the pre-processed dataset symmetric for retrospective analysis, wherein the data transformation for retrospective analysis result from processing by the at least one time window dataset with a hazard function defined by: G t = α * ln( G - β) - α * ln( α), wherein parameter α = T c - β and parameter β = D r - 1, in which T c is a center of a transformed space, D r is a minimum defined glucose level, G t is the transformed data of blood glucose concentration measurements provided in the dataset, and G represents original glucose level values of the blood glucose concentration measurements provided in the at least one time window dataset and measured in millimoles per liter, - build a similarity matrix from the pre-processed dataset, comprising computing distances between each pair of similar time series of data presented in the pre-processed dataset, and - output an optimum number of similarity clusters, which comprise time series, found by the processor from the similarity matrix, wherein an output of the similarity matrix process is used in an agglomerative clustering process to output similarity clusters, the agglomerative clustering process having the following pseudo code: (a) Compute a distance matrix between data points of the output; (b) Let each of the data points be a cluster; (c) Repeat following: i. Merge two closest clusters and ii. Update the distance matrix; and (d) Do Repeat until only a single cluster remains, wherein the clustering is used to identify problem times along the day and help optimize therapy that focuses on these weak points. - a physiological data input device which acquires under unsupervised conditions a plurality of physiological measurements of the patient within a time window to generate at least one time window dataset of collected unsupervised daily monitoring profiles; - a memory storing an unsupervised daily monitoring profile clustering algorithm; and - a processor in communication with said input device to receive said generated at least one time window dataset, and in communication with said memory in order to execute said unsupervised daily monitoring profile clustering algorithm, characterized in that said unsupervised daily monitoring profile clustering algorithm when executed by said processor causes said processor automatically to: - pre-process the at least one time window dataset to control an amount of bias/aggressiveness of a penalty in the collected unsupervised daily monitoring profiles due to the unsupervised conditions via a data transformation of the at least one time window dataset to generate a pre-processed dataset which blunts responses on hyper-side and/or hypo-side of a glucose response spectrum of a time series from the at least one time window dataset, wherein the data transformation makes the pre-processed dataset symmetric for retrospective analysis, wherein the data transformation for retrospective analysis result from processing by the at least one time window dataset with a hazard function defined by: G t = α * ln( G - β) - α * ln( α), wherein parameter α = T c - β and parameter β = D r - 1, in which T c is a center of a transformed space, D r is a minimum defined glucose level, G t is the transformed data of blood glucose concentration measurements provided in the dataset, and G represents original glucose level values of the blood glucose concentration measurements provided in the at least one time window dataset and measured in millimoles per liter, - build a similarity matrix from the pre-processed dataset, comprising computing distances between each pair of similar time series of data presented in the pre-processed dataset, and - output an optimum number of similarity clusters, which comprise time series, found by the processor from the similarity matrix, wherein an output of the similarity matrix process is used in an agglomerative clustering process to output similarity clusters, the agglomerative clustering process having the following pseudo code: (a) Compute a distance matrix between data points of the output; (b) Let each of the data points be a cluster; (c) Repeat following: i. Merge two closest clusters and ii. Update the distance matrix; and (d) Do Repeat until only a single cluster remains, wherein the clustering is used to identify problem times along the day and help optimize therapy that focuses on these weak points.