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Extracting ranking from pairwise comparison data has been very popular these days especially due to the huge source of comparison data available in the Internet. One of the many ways to collect a large amount of data from ordinary users is crowd sourcing. One example is reCaptcha, which converts scanned text images into text by using human recognition capability of a huge number of people.With the...
In this paper, we applied two methods of process mining techniques (from Discovery class/approach) in order to extract knowledge from event logs recorded by an online information system. The event log was created via information received from an online proceedings review system in Thailand. Accordingly, Alpha and Heuristic algorithms were used with the objective of automatically visualizing the models...
Attribute reduction with rough sets aims to delete superfluous condition attributes from a decision system by considering the inconsistency between condition attributes and the decision labels. However, heterogeneous condition attributes including symbolic and real-valued ones always coexist for most decision systems and different types of attributes induce different kinds of granular structures....
Data mining research has produced a significant repertoire of algorithms to predict the classification of data instances with reasonable accuracy. However, data quantity and availability is continuing to rapidly expand such that we no longer have fixed and manageable data sets, but rather continual streams of data. Mining streaming data becomes challenging when using a piece-wise or online approach,...
Movement data have been widely collected from GPS and sensors, allowing us to analyze how moving objects interact in terms of space and time and to learn about the relationships that exist among the objects. In this paper, we investigate an interesting relationship that has not been adequately studied so far: the following relationship. Intuitively, a follower has similar trajectories as its leader...
This paper introduces GLS Miner, a novel method for discovering process models from event logs using guided local search. It is shown that GLS Miner can discover process models that correctly map to the event log. GLS Miner works with business processes represented as graphs, and the final discovered process is represented as a BPMN diagram.
Aiming at the shortage of EFCBA which is sensitive to the learning set and short of self-learning ability, this paper proposes optimized classification algorithm based on self-learning and the calculated method of threshold, that has the ability of self-optimiz ation. At the same time, the optimiz ation model of cluster center was proposed, which implements the coordination among various methods....
In the past, the multiple fuzzy frequent pattern tree (MFFP tree) was proposed for extracting multiple fuzzy frequent itemsets from quantitative transactions. It kept the multiple transformed fuzzy regions of an item to form the multiple fuzzy frequent itemsets. In this paper, an incremental algorithm is proposed for efficiently mining multiple fuzzy frequent itemsets based on the FUP concepts and...
This paper presents a novel depth map generation method based on geometric information. Our method divides a 2d image into two parts—the foreground and the background. Through extracting the predominant lines and vanishing point of the background, the background depth map is determined. Then we use this depth map as a ‘scalar’ to measure the depth value of the foreground object. Finally, the depth...
In this paper, we propose a new method for constructing decision trees based on Ant Colony Optimization (ACO). The ACO is a metaheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest...
This article proposed an algorithm for solving traveling salesman problem from the perspective of geometry. At a given planar point distribution, search remote border points and connect these points to form a polygon circuit called outer ring including all the points, according to the same principle, search a polygon inner ring circuit among the points that aren't in outer ring. According to the principle...
In this study, we propose a clustering technique based on FP-tree algorithm to group students based on the intended courses they will register for a given next semester. The goal of this clustering is to solve the problem of course's time scheduling that we encountered in previous semesters which prevented students from enrolling in some of these courses as they are being scheduled at the same time...
Identifying outliers is a difficult thing in data mining. We adopt the notion of deviants for outliers in data streams. Deviants are data set whose removal from the data sequence over data streams lead to sum of error SSE minimize. We present DDA algorithm to detect deviants over massive data streams. With this algorithm the histogram can more accurately determine the deviants and greatly reduce error.
Considering the parking guidance in the management systems of large-scale parking lots, in this study, a novel approach based on particle swarm optimization was presented. A diversity factor is introduced in the PSO to modify the inertia weight which improves the diversity of solution. Furthermore, the position updates formula of PSO is restructured by the SDE strategy. The global searching capability...
In this paper, we study the problem of graph coloring and propose a novel particle swarm optimization (PSO) algorithm for it. We use the PSO evolutionary progress to improve a simple deterministic greedy algorithm. The new algorithm can achieve a result that is better than known heuristic algorithms do, as verified by an extensive simulation study.
How to mine outliers of online data streams in a short time is an unsolved problem. We propose a new outlier factor metric whose name is the frequent pattern contradiction outlier factor called FPCOF for short. FPCOF can easily measure the degree to which each data instance in data streams is considered as an outlier. In order to compute FPCOF, we construct an outlier detection tree (or OD-tree in...
In this paper, we focus on a single graph whose vertices contain a set of quantitative attributes. Several networks can be naturally represented in this complex graph. An example is a social network whose vertex corresponds to a person with some quantitative items such as age, salary and so on. Although it can be expected that this kind of data will increase rapidly, most of current graph mining algorithms...
Applying the concept of organizational structure to social network analysis may well represent the power of members and the scope of their power in a social network. In this paper, we propose a data structure, called Community Tree, to represent the organizational structure in the social network. We combine the PageRank algorithm and random walks on graph to derive the community tree from the social...
Archives digitization refers to the transforming process of traditional archives to digital archives. Digital archives can, to a greater extent, meet the public demand for accessing to the file information, improve the utilization of archives and its society status, and strengthen its function of cultural institutions. So, It has important practical significance to quantitatively research on archives...
Evaluation on administrators of colleges and universities can provide a scientific basis for decision making on personnel assessment, selection, and appointment of cadres for organization departments. In talent evaluation process there are a number of vague concepts and various factors. Although we have the severity of the primary and secondary points, the ambiguity also exists between subjects and...
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