Data Mining and Knowledge Discovery Handbook, 2 Edition part 109. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 1060 Chotirat Ann Ratanamahatana et al. errors. While the algorithm is perhaps the most commonly used clustering algorithm in the literature one of its shortcomings is the fact that the number of clusters K must be prespecified. Clustering has been used in many application domains including biology medicine anthropology marketing and economics. It is also a vital process for condensing and summarizing information since it can provide a synopsis of the stored data. Similar to query by content there are two types of time series clustering whole clustering and subsequence clustering. The notion of whole clustering is similar to that of conventional clustering of discrete objects. Given a set of individual time series data the objective is to group similar time series into the same cluster. On the other hand given a single typically long time series subsequence clustering is performed on each individual time series subsequence extracted from the long time series with a sliding window. Subsequence clustering is a common pre-processing step for many pattern discovery algorithms of which the most well-known being the one proposed for time series rule discovery. Recent empirical and theoretical results suggest that subsequence clustering may not be meaningful on an entire dataset Keogh et al. 2003 and that clustering should only be applied to a subset of the data. Some feature extraction algorithm must choose the subset of data but we cannot use clustering as the feature extraction algorithm as this would open the possibility of a chicken and egg paradox. Several researchers have suggested using time series motifs see below as the feature extraction algorithm Chiu et al. 2003 . Prediction Forecasting Prediction can be viewed as a type of clustering or classification. The difference is that prediction is predicting a future state rather than a current one. Its applications include obtaining forewarning of natural disasters flooding hurricane snowstorm etc epidemics .
