Clustering data described by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical attribute, since the values are not ordered. In this article, we propose a framework to learn a context-based distance for categorical attributes. The key intuition of this work is that the distance between two values of a categorical attribute A _i can be determined by the way in which the values of the other attributes A _j are distributed in the dataset objects: if they are similarly distributed in the groups of objects in correspondence of the distinct values of A _i a low value of distance is obtained. We propose also a solution to the critical point of the choice of the attributes A _j . We validate our approach by embedding our distance learning framework in a hierarchical clustering algorithm. We applied it on various real world and synthetic datasets, both low and high-dimensional. Experimental results show that our method is competitive with respect to the state of the art of categorical data clustering approaches. We also show that our approach is scalable and has a low impact on the overall computational time of a clustering task.

We present two approaches for digital twinning in the context of the forecast of power production by photovoltaic panels. We employ two digital models that are complementary: the first one is a cyber-physical system, simulating the physical properties of a photovoltaic panel, built by the open- source object-oriented modeling language Modelica. The second model is data-driven, obtained by the application of machine learning techniques on the data collected in an installation of the equipment. Both approaches make use of data from the weather forecast of each day. We compare the results of the two approaches. Finally, we integrate them in more sophisticated hybrid systems that get the benefits of both.

In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.

A new model to evaluate dependencies in data mining problems is presented and discussed. The well-known concept of the association rule is replaced by the new definition of dependence value, which is a single real number uniquely associated with a given itemset. Knowledge of dependence values is sufficient to describe all the dependencies characterizing a given data mining problem. The dependence value of an itemset is the difference between the occurrence probability of the itemset and a corresponding “maximum independence estimate.” This can be determined as a function of joint probabilities of the subsets of the itemset being considered by maximizing a suitable entropy function. So it is possible to separate in an itemset of cardinaltiy k the dependence inherited from its subsets of cardinality (k - 1) and the specific inherent dependence of that itemset. The absolute value of the difference between the probability p( i ) of the event i that indicates the prescence of the itemset {a,b,... } and its maximum independence estimate is constant for any combination of values of 〈a,b,...〉. In addition, the Boolean function specifying the combination of values for which the dependence is positive is a parity function. So the determination of such combinations is immediate. The model appears to be simple and powerful.