The Lorenz Dominance Order As a Measure Of . . .

Ranking summaries generated from databases is useful within the context of descriptive data mining tasks where a single data set can be generalized in many different ways and to many levels of granularity. Our approach to generating summaries is based upon a data structure, associated with an attribute, called a domain generalization graph (DGG). A DGG for an attribute is a directed graph where each node represents a domain of values created by partitioning the original domain for the attribute, and each edge represents a generalization relation between these domains. Given a set of DGGs associated with a set of attributes, a generalization space can be defined as all possible combinations of domains, where one domain is selected from each DGG for each combination. This generalization space describes, then, all possible summaries consistent with the DGGs that can be generated from the selected attributes. When the number of attributes to be generalized is large or the DGGs associated with the attributes are complex, the generalization space can be very large, resulting in the generation of many summaries. The number of summaries can easily exceed the capabilities of a domain expert to identify interesting results. In this paper, we show that the Lorenz dominance order can be used to rank the summaries prior to presentation to the domain expert. The Lorenz dominance order defines a partial order on the summaries, in most cases, and in some cases, defines a total order. The rank order of the summaries represents an objective evaluation of their relative interestingness and provides the domain expert with a starting point for further subjective evaluation of the summaries

No-Limit Texas Hold'em Poker agents created with evolutionary neural networks

Abstract — In order for computer Poker agents to play the game well, they must analyse their current quality despite imperfect information, predict the likelihood of future game states dependent upon random outcomes, model opponents who are deliberately trying to mislead them, and manage finances to improve their current condition. This leads to a game space that is large compared to other classic games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems such as Poker. In this paper, we develop No-Limit Texas Hold’em Poker agents using a hybrid method known as evolving neural networks. We also investigate the appropriateness of evolving these agents using evolutionary heuristics such as co-evolution and halls of fame. Our agents were experimentally evaluated against several benchmark agents as well as agents previously developed in other work. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., no co-evolution) using a large hall of fame. These results demonstrate an effective use of evolving neural networks to create competitive No-Limit Texas Hold’em Poker agents. I

Knowledge discovery and interestingness measures: A survey

Knowledge discovery in databases, also known as data mining, is the efficient discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. An important problem in the area of data mining is the development of effective measures of interestingness for ranking the discovered knowledge. In this report, we provide a general overview of the more successful and widely known data mining techniques and algorithms, and survey seventeen interestingness measures from the literature that have been successfully employed in data mining applications. 1