Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In paper, we propose address issue by using the Inverse Covariance Clustering (ICC) method identify inherent market states and then integrate such into a dynamic optimization process. Extensive experiments across three different markets, NASDAQ, FTSE HS3...

We study two generalizations of classic clustering problems called dynamic ordered k-median and k-supplier, where the points that need evolve over time, we are allowed to move cluster centers between consecutive time steps. In these problems, general goal is minimize certain combinations service cost movement centers, or one subject some constraints on other. obtain a constant-factor approximat...

Clustering and visualizing high dimensional dynamic data is a challenging problem in the data mining. Most of the existing clustering algorithms are based on the static statistical relationship among data. In the clustering process there are no predefined classes and no examples that would show what kind of desirable relations should be valid among the data. This paper gives existing work done ...