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Recent developments in the storage of system data in the Navy's data repository, LEAPS, using the FOCUS product meta-model have opened the doors to graph-theory applications in the design of Navy ship systems in the early stages of design. In this paper, we demonstrate the ability to extract graphs from ship data and present pertinent applications of such graphs including a vulnerability metric for...
Fuzzy association rules are one of the most important data mining techniques. They allow to discover useful and meaningful information that help in decision-making. Many algorithms have been proposed to extract fuzzy association rules. A major drawback of these proposed algorithms is their high run-time for extracting fuzzy association rules. To overcome this problem, we introduce in this paper a...
A standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which...
Traditional multi-step attack correlation approaches based on intrusion alerts face the challenge of recognizing attack scenarios because these approaches require complex pre-defined association rules as well as a high dependency on expert knowledge. Meanwhile, they barely consider the privacy issues. Under such circumstance, a novel algorithm is proposed to construct multi-step attack scenarios based...
Extracting unknown and possibly useful information from a set of examples that has desired features is crucial and important for data analysis and interpretation. Normally, a public repository has become the most used method in attempting to find a suitable domain. However, relying on the available data in the public repository has several disadvantages. In this case, an automatic problem generation...
The present paper allows to recommend data corresponding to a user requirement and an analysis algorithm which is able to analyze the data and let an analysis service selected by the user among a plurality of analysis services be automatically performed in a big data platform.
Frequent itemsets which are quite useful in many applications always suffer from their huge number and information redundancy. Frequent closed itemsets that provide a minimal and lossless presentation of all frequent itemsets are a solution to the problem. In past years, frequent closed itemsets mining (FCIM) has been extensively studied and many effective FCIM algorithms have been proposed. CHARM...
Frequent itemsets mining will not conducive to data analysis. Because of frequent itemsets mining produce very large amount of frequent itemsets in the case large data. Frequent closed itemsets provides a lossless frequent item sets, the smallest representation. This paper aims at the problem of shortage of the DCI_Closed algorithm in the process of mining efficiency, puts forward a kind of improved...
Mining sequential rules helps discover useful sequences in sequence databases quickly and efficiently. Most of the proposed algorithms, however, focus on generating all possible sequential rules. That will produce a lot of redundant rules, affecting efficient mining. In order to solve this problem, mining non-redundant sequential rules has thus been presented lately. However, the algorithms proposed...
Real datasets always play an essential role in graph mining and analysis. However, nowadays most available real datasets only support millions of nodes. Therefore, the literature on Big Data analysis utilizes statistical graph generators to generate a massive graph (e.g., billions of nodes) for evaluating the scalability of an algorithm. Nevertheless, current popular statistical graph generators are...
Distribution of data stream is always changed in the real world. This problem is usually defined as concept drift [1]. The state-of-the-art decision tree classification method CVFDT[2] can solve the concept drift problem well, but the efficiency is debased because of its general method of handling instances in CVFDT without considering the types of concept drift. In this paper, an algorithm called...
Analyzing databases with many attributes per object is a recent challenge. For these high dimensional data it is known that traditional clustering algorithms fail to detect meaningful patterns. As a solution subspace clustering techniques were introduced. They analyze arbitrary subspace projections of the data to detect clustering structures. In this demonstration, we introduce the first subspace...
Association rule mining aims at generating association rules between sets of items in a database. Now a day, due to huge accumulation in the database technology, the data are representing in the high dimensional data space. However, it is becoming very tedious to generate association rules from high dimensional data, because it contains different dimensions or attributes in the large data bases. In...
Differential Evolution (DE) is a numerical optimization approach, which is simple to implement, requires little parameter tuning, and known for remarkable performance. It mainly uses the distance and direction information from the current population to guide its further search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool...
A flexible division method of customer value based on affiliation cloud clustering algorithm is proposed in order to solve the defects of the "hard division" about e-commerce customer value.The method puts cloud model into the qualify evaluation of the e-commerce customer value. Effectly divides the e-commerce customer value by affiliation cloud clustering algorithm and a uncertainty expression...
Crosscutting concerns cannot be well modularized in object-oriented software. The implementation of a crosscutting concern is typically scattered over many locations and tangled with the implementation of other concerns. The presence of crosscutting concerns is one of the major problems in software understanding and evolution. Aspect-oriented programming offers mechanisms to factor them out into a...
In this paper, we partition the association rule set into disjoint equivalence rule classes. Each of them contains rules having the same confidence and then it is split into basic and consequence rule sets based on the order relation on it. Basic rule set, which includes minimal elements according to this relation, is directly found by our algorithm MG_BARS. In addition, by adding appropriate eliminable...
Formal Concept Analysis and Rough Set Theory offer related and complementary approaches for data analysis. Many efforts have been made to compare and combine the two theories. In this paper, a transformation design is proposed from a partition to concept lattice. This method selects the max-covered attributes decision-set from the equivalence classes to assist generating the new concepts and an index-tree...
Discovering global frequent subtrees from ordered labeled trees in distribute environment is an attractive research problem in data mining. In this paper, a new algorithm FAMDFS (Fast Algorithm for Mining Global Frequent Subtree) was proposed. This algorithm transfer local projected branch frequent nodes, can decrease network traffic, improve the efficiency of the algorithm. Theoretical analysis and...
Data mining (DM) is the process of automated extraction of interesting data patterns representing knowledge, from the large data sets. Frequent itemsets are the itemsets that appear in a data set frequently. Finding such frequent itemsets plays an essential role in mining associations, correlations, and many other interesting relationships among itemsets in transactional database. In this paper an...
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