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With the development of cyber threats on the Internet, the number of malware, especially unknown malware, is also dramatically increasing. Since all of malware cannot be analyzed by analysts, it is very important to find out new malware that should be analyzed by them. In order to cope with this issue, the existing approaches focused on malware classification using static or dynamic analysis results...
Based on the development and application of on-board subsystem test bench for current CTCS-3 system, this paper focuses on the approach of automatically generation of test sequence, takes the existing test sequences of ETCS-2 (European Train Control system level 2) as the train set existing relatively mature test sequence as the training set, to execute association rule mining. The whole data mining...
This paper presents some preliminary work on detecting sex offenders who might register in social networks under assumed identities using data mining, string matching, and weight-biased scoring.
Frequent sequence mining methods often make use of constraints to control which subsequences should be mined, e.g., length, gap, span, regular-expression, and hierarchy constraints. We show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. In more detail, we propose a set of simple and intuitive "pattern expressions"...
The demand for map services has risen significantly in recent years due to the popularity of mobile devices and wireless networks. Since there are always emerging point-of-interest (POI) in the real world, mining POIs shared by users from the Web has been a challenging problem to enrich existing POI database. However, crawling address-bearing pages and extracting POI relations are only the fundamentals...
Frequent subgraph mining, i.e. enumeration of subgraphs appearing frequently in graph databases, is one of the most fundamental problems in graph mining. Several optimization techniques as well as parallel implementations are developed to alleviate the problem that subgraph miners require a long computation time if target databases are huge or given frequency threshold is low. In this paper, we investigate...
Hiding High Utility Sequential Patterns (HUSPs) is the task of finding the ways how to hide high utility sequential patterns appearing in sequence databases so that the adversaries cannot discover them after hiding. It has become an important research topic in recent years and has been applied in various domains such as business, marketing, stock, health and security, etc. However, few methods have...
In today's scenario of data mining, there are so many upgraded versions of traditional Apriori has been launched in association due to its limitation of suffering from number of inefficiencies. Which have procreate other algorithms. The actual concept of this research topic is also one of them and it mainly focus on the description of the new version of hash based association using association rule...
Almost all activities observed in nowadays applications are correlated with a timing sequence. Users are mainly looking for interesting sequences out of such data. Sequential pattern mining algorithms aim at finding frequent sequences. Usually, the mined activities have timing durations that represent time intervals between their starting and ending points. The majority of sequential pattern mining...
Smart home is one of the most important applications of ubiquitous computing. In this work, we propose an infrastructure of Vietnamese Smart homes as well as a training framework for activity recognition and forecast. In this framework, active learning technique is applied and a new mining algorithm is proposed. In addition to activity recognition, a forecast mechanism is also added into the smart...
The location of business is indispensable for all commercial activities. However, current partition of commercial area mostly depends on human experience and other subjective factors rather than intelligent decisions, which is likely to mislead people who want to engage in business. The new combination of algorithms in this paper aims to clarify how the commercial area is formed by visualizing whether...
Chronic diseases may cause heavy burden on health care resources and disturb the quality of life. Chronic Obstructive Pulmonary Disease (COPD) is an important chronic disease, which takes a long period of time to progress and hard to detect in early stage. In this work, we propose a novel approach for early assessment on COPD by mining COPD-related sequential risk patterns from diagnostic clinical...
In this paper, we developed a Binary Particle Swarm Optimization (BPSO) based fuzzy association rule miner to generate fuzzy association rules from a transactional database by formulating a combinatorial global optimization problem, without pre-defining minimum support and confidence unlike other conventional association miners. Goodness of fuzzy association rules is measured by a fitness function...
With the development of various new technologies in the field of network technology and also cloud computing, there is also lots of data that is involved in all of it. Thus the concept of big data is introduced which is not an entirely new technology but an extension to Data Mining. Thus securing this Big Data is both important as well as challenging. Since the size of the data is very large it is...
This paper proposes a fuzzy relational clustering (FRC) to find similar sentences from a set of sentences as well as group them in clusters. For finding similar sentences here FRC used both word-to-word and order similarity. For word-to-word similarity FRC used Jiang and Conrath similarity measure (JnC) with the help of WordNet database. Order similarity is calculated from joint word set. As a sentence...
Database-centric applications (DCAs) usually contain a large number of tables, attributes, and constraints describing the underlying data model. Understanding how database tables and attributes are used in the source code along with the constraints related to these usages is an important component of DCA maintenance. However, documenting database-related operations and their constraints in the source...
Frequent subgraph mining is useful in most knowledge discovery tasks such as classification, clustering and indexing. Many algorithms and methods have been developed to mine frequent subgraphs. To have an understanding of several mining frequent subgraph algorithms, it is advantageous to establish a common framework for their study. In this paper, we propose a comparative study of several approaches...
Targeting human activities responsible for the energy consumption instead of focusing solely on single appliance feedback for achieving energy efficiency in residential homes would link human behaviors to the resulting energy consumption. To this end, learning when appliances are in an active or idle state and the related user activity is crucial. Until smart appliances become widespread and can communicate...
A new type of linguistic summaries, so-called contextual linguistic summaries, are further developed. The point of departure are contextual bipolar queries which play the same role for a new type of summaries as flexible fuzzy queries do with respect to the classical linguistic summaries. The bipolar queries employed are of a special type, following the required/desired semantics formalized using...
Recently α-cut irreducible and δ1δ2-multi-adjoint concept lattices have been introduced as two different methodologies focus on reducing the size of a given fuzzy concept lattice. The philosophy of both methodologies is completely different and so, the obtained lattices too. This paper analyzes the differences and proposes that the best is to combine both methodologies in order to obtain new procedures...
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