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We present a method of learning relationships at the triadic level of a relationship network. The method proposes learning linkages of a particular network using a Support Vector Machine (SVM) classifier trained on the known part of a relationship network. Using features drawn from the topological information of the two degrees of separation of a link a classifier learns whether two people of that...
We propose an axiomatic approach to defining of the validity of probabilistic inductive rules E H. The set of rules is evaluated against an available dataset, where the conditions E, H are either true or false for each instance in the dataset. Introduced here are six axioms which formalize common sense dependencies between the validity of rules and their support, confidence, lift and amount of available...
Recently the academic communities have paid more attention to the queries and mining on uncertain data. In the tasks such as clustering or nearest-neighbor queries, expected distance is often used as a distance measurement among uncertain data objects. Traditional database systems store uncertain objects using their expected (average) location in the data space. Distances can be calculated easily...
We present K2GA, an algorithm for learning Bayesian network structures from data. K2GA uses a genetic algorithm to perform stochastic search, while employing a modified version of the K2 heuristic to score proposed networks and improve future generations. We show each component of K2GA, a combination of these components to form the basic algorithm, extensions to the algorithm for improved accuracy,...
In this paper, we present an extension of the well known algorithm for association mining, Apriori. This extended algorithm, ApriorBL, considers associations between items which occur together - focusing solely on the borderline cases. These borderline cases occur often enough to provide valuable information; however, there are currently no algorithms that target them. We discuss how the AprioriBL...
For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business school's accreditation...
There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective is, given a series of observations, to predict exactly when certain interactions will occur in the future. We propose a representation for this type of temporal data and a generic, streaming, adaptive algorithm to predict the...
The association rule mining task identifies all the intrinsic associations among the items contained in data and leads to only specialized knowledge. To overcome this problem the generalized association rules appeared. This type of rule associates not only the items contained in data, but also some items encoded into a given taxonomy. Therefore, the techniques used to obtain generalized association...
There are many data mining systems derived from machine learning, neural network, statistics and other fields. Most of them are dedicated to some particular algorithms or applications. Unfortunately, their architectures are still too naive to provide satisfactory background for advanced meta-learning problems. In order to efficiently perform sophisticated meta-level analysis, we need a very versatile,...
Feature selection contributes to increasing many learners' accuracy by identifying and removing irrelevant features in multidimensional datasets. Conventional feature selection methods determine the optimal feature subset independently from and prior to the introduction of a new query. In general, some features will be relevant only in certain tasks. We argue that a query, as an indicator of the attention...
Missing data in databases are considered to be one of the biggest problems faced on data mining application. This problem can be aggravated when there is massive missing data in the presence of imbalanced databases. Several techniques as samples deletion, values imputation, values prediction through classifiers and approximation of patterns have been proposed and compared, but these comparisons do...
Locally Linear Embedding (LLE) is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. This paper mainly proposes a hierarchical framework manifold learning method, based on LLE and Growing Neural Gas(GNG), named Growing Locally Linear Embedding(GLLE). First, we address the major limitations of the original LLE: intrinsic dimensionality...
The Counterpropagation network is a combination of competitive network (Kohonen layer) and Grossberg outstar structure. In this paper we have proposed a complex valued representation on conventional forward only counterpropagation network. Many researchers have investigated the computational capabilities of neuron models for real values only. The novel part of the paper is, while considering the complex...
This paper presents a prototype-driven framework for classifying evolving data streams. Our framework uses cluster prototypes to summarize the data and to determine whether the current model is outdated. This strategy of rebuilding the model only when significant changes are detected helps to reduce the computational overhead and the amount of labeled examples needed. To improve its accuracy, we also...
We report on the application of an evolutionary algorithm to a noisy, dynamic optimization problem in chemistry: the maximization of three-photon absorption in molecular iodine. An evolution strategy is used in real-time in a closed loop experiment to search the space of physically realizable phase-modulated femtosecond laser pulses. The probability of three-photon absorption is estimated by measuring...
In this paper we compared and analyzed four graph induction methods to automatically classify spoligotypes. A spoligotype is a sequence of 43 binary values provided by a DNA analysis technique. This method is known to be useful and efficient to many supervised learning problems. We found it interesting to use these techniques especially for sequential data, in order to create a classifier based on...
Reliable prediction of sales can improve the quality of business strategy. This research develops a hybrid model by integrating K-mean cluster and Fuzzy Back Propagation Network (KFBPN) to forecast the future sales of a printed circuit board factory. Based on the K-mean clustering technique, the historic data can be classified into different clusters, thus the noise of the original data can be reduced...
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