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This paper aims to propose and discuss concepts of how users can recognise information seeking behaviour automatically and what implications such an automatic recognition can have. The authors develop the discussion around variables proposed in Wilson's second model of information behaviour and state how they can collect data necessary to recognise information behaviour automatically. The authors...
Traumatic brain injury (TBI) and its complications, including intracranial hypertension, are one of the leading causes of mortality. Many proposed algorithms have attempted to overcome the invasiveness of intracranial pressure monitoring with limited clinical applications. In medical practices, changes of intracranial hypertension are perceived manually, by clinical experts, via surgical placement...
This work presents a novel writing style-based approach to detect multi-account users on User-Generated Content (UGC) sites. Unlike existing works which emphasize feasibility and privacy leakage, we focus on precise writing style-based multi-account detection. Specifically, we leverage a one-class classification-based approach to detect multi-account behaviors, in which a mutual similarity measurement...
Automatic identification of emotions from text has been an interesting area of research in the recent times. With the exponentially growing number of online users, emotion identification of the text being read by the users can help in providing a comforting environment to the readers. In this paper, emotion classification is attempted on the news articles from the society channel of Sina. The proposed...
Weighted item-set mining is used to find the profitable connection between the data. There are two types of items contained in dataset i.e. frequent and infrequent. Infrequent item-sets are nothing but items which are rarely found in database. Mining frequent items in data mining are very helpful for retrieving the related data present in the dataset. Using transactional dataset as an input dataset...
In this research study, our major interest is to test the significant association between selected variables which otherwise invisible. These variables have indirect impact on performance of students. We have devised out our own dataset for the experimental purpose. Our study has made these variables and their relationship visible. The results enable us to determine characteristics of learning environment...
Binary relevance (BR) is a well-known framework for multi-label classification. It decomposes multi-label classification into binary (one-vs-rest) classification subproblems, one for each label. The BR approach is a simple and straightforward way for multi-label classification, but it still has several drawbacks. First, it does not consider label correlations. Second, each binary classifier may suffer...
Mining based on opinions can extract useful information from users' comments. After doing cluster and analysis on the information, users can get a detailed understanding of the commodity, then determine to buy the commodity or not. In this paper, firstly, we extract evaluation objects and evaluation words, then cluster the evaluation objects. Next based on SO-PMI algorithm, judge the polarity of evaluation...
Using the application of mathematical statistics analysis theory, this paper presents a related data mining analysis model based on the Pearson's r. We introduced Pearson's r to mine association rules of distinctively related courses. Thus we build the computer aided teaching evaluation system, and then draw a useful conclusion for teaching.
Emotions serve as a communicative function both within the brain and within the social group. Most of previous opinion mining studies applied on Arabic microblog text to identify positive, negative or neutral polarity. This paper studies the problem of detecting multiple emotion classes in Arabic microblog text (e.g. Twitter). Incoming Arabic microblog text is classified into one of fine grained emotional...
In this paper a game for working memory assessment in children between 4 to 7 years old is introduced. We evaluate the validity of the game as an assessment tool by comparing its results on a group of pre-school children with a commercially validated working memory test. Since the correctness of an assessment highly depends on the attentional state of the subject, several algorithms have been developed...
Association rule mining (ARM) is a well-researched domain in the field of data mining. It is seen as a problem of predicting customers purchasing behavior, popularly known as “Market Basket Analysis”. This problem can be solved by using Apriori algorithm which is majorly 3-steps (Joining, Pruning and Verification) process. In this paper, an alternate to Apriori algorithm's pruning step is proposed...
In this paper we present a novel technique called iDMI that imputes missing values of a data set by combining a decision tree algorithm (DT) and an expectation-maximization (EMI) algorithm. We first divide a data set into horizontal segments through applying a DT algorithm such as C4.5, and then apply an EMI algorithm on each segment in order to impute the missing values belong to the segment. If...
The prevalent use of computer applications and communication technologies has rising the numbers of network intrusion attempts. These malicious attempts including hacking, botnets and works are pushing organization networks to a risky atmosphere where the intruder tries to compromise the confidentiality, integrity and availability of resources. In order to detect these malicious activities, Intrusion...
The purpose of this research was to identify which course tracking variables correlate significantly with academic performance in blended asynchronous online courses through an empirical analysis of Learning Management System (LMS) data. In this study, course tracking variables refers to number of online sessions, number of original posts created, number of follow-up posts created, number of content...
Low support makes dramatic increase in the number of itemsets and brings less efficient frequent itemset mining. Correlation measures introduced to restrict the number of frequent itemsets generated in order to improve the efficiency of mining under certain conditions. An improved FP-Tree algorithm using node linked list FP-Tree is proposed. This algorithm exploits efficient pruning strategies using...
Detecting and identifying security events to provide cyber situation awareness has become an increasingly important task within the network research and development community. We propose a graph similarity-based approach to event detection and identification that integrates a number of techniques to collect time-varying situation information, extract correlations between event attributes, and characterize...
User requirements obtained through text data mining are very important to improve the competitiveness of enterprises. In this paper an algorithm of acquiring user requirements in machinery products by using text association rule is proposed. In the algorithm, the user requirement documents are represented by vector space model. The feature words matrix is obtained by transposing the documents matrix...
In mining association rules, Item sets with high-length usually has lower support, but still have potential value. To mine efficacious association rules under long-pattern, a new mining method of efficacious association rules is proposed under length-decreasing support constraint. Compare to other mining methods of association rules, the new method can mine more efficacious long-patterns and improve...
The previous study of pattern discovery in storage systems focus on sequential pattern (SP) mining in lower level traces, but they don't scale well to the application level. For patterns in application level are mostly composed of Contiguous Item Sequential Patterns (CISP) which are much simpler than SP, so it's inefficient for the previous studies to mine CISP with clumsy SP mining algorithms. We...
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