The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Big data applications consist of very large collection of small records, for example data from a retail website, data from movie streaming services, sensor data applications and many other such applications. Frequent item set mining is one of the common tools used for all these applications to generate recommendations to improve user experience of the website. Frequent itemset mining is also used...
With the development of computer network technology and popularization, and the technology of data acquisition, data management and data query develop rapidly. People who is in the surging "data ocean", need an intelligent technology badly, to "explore" more valuable "oil "from the "data ocean",so the data mining technology which has been successfully used in...
Many association rule mining algorithms find associations and correlations from traditional transaction databases, in which the content of each transaction is definitely precise. However, due to instrument errors, imprecise of sensor monitoring systems, and so on, real-world data tend to be numerical data with inherent uncertainty. To deal with these situations, we propose a FP-growth-based mining...
With the rapid development of computer technology, web services has been widely used. In these applications, the uncertain data is in the form of streams. In view of this kind of situation, present a new generalized data structure, that is, PSUF - tree, to store uncertain data streams, all itemsets in recent window are contained in global PStree in a condensed format, establish a header table in which...
There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However patternminingtechniquesoftenproducemanypatternsthathave to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are...
Road traffic condition in cities are complicated by the daily, weekly, seasonally, and weather-induced traffic demand fluctuations and the effects caused by the control of traffic signals. Therefore, it is difficult to quantitatively analyze typical traffic congestion patterns that are represented by the time and place of occurrence, the process of propagation and diminution, duration time, and many...
Association rules mining is a data mining technique that seeks interesting associations between attributes from massive high-dimensional categorical feature spaces. However, as the dimensionality gets higher, the data gets sparser which results in the discovery of a large number of association rules and makes it difficult to understand and to interpret. In this paper, we focus on a particular type...
In recent years, data mining techniques has attracted the attention from educational researchers and applied in educational research pervasively. As a famous data mining method, traditional association rules mining tend to ignore the infrequent data item and can only analyze a single dataset. To address these issues, a contrast targeted rule mining model is introduced in this paper. A complete analysis...
To increase the learning effectiveness and willingness of students became the most important issue for the Universities in Taiwan. Therefore, we must find the important factors of the learning effectiveness to improve the learn willingness of students. However, it is not easy to measure the learning effectiveness because the subjective judgment of evaluators and the attributes of factors are always...
Data mining, through association rules mining, is one of the best known approaches for patterns identification. However, it results most of the time in a huge set of patterns (rules), so their exploitation is not easy and often requires expert analysis. In this paper we describe a new pattern "set of contrasting rules" which, contrary to most state-of-the-art patterns, has the characteristic...
Frequent itemset mining is one of the most common of data mining tasks. In its simplest form, one is given a table of data in which the columns represent attributes and each row specifies a value for each attribute, each attribute-value pair being referred to as an item. The task is to find sets of these items that occur frequently in the data, where frequency is specified as a minimum occurrence...
We introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value. This problem consists in generating the frequent itemsets with a maximum penalty threshold. We then provide a propositional satisfiability based encoding. We extend the...
Knowledge discovery in big data is one of most interesting topics in state-of-the-art research, and frequent patterns mining is a major task. With the rapid growth of modern technology, high volumes of data—which are of different veracities (i.e., may be precise or uncertain)—are flowing at a high velocity all over the world. Properties of data temporally changes with changes in the people's interests,...
Discovering useful patterns plays an essential role in data management and data mining. Frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied on (standard) precise transaction databases. Uncertain transaction databases consist of sets of existentially uncertain items. The uncertainty of items in transactions makes traditional...
Association rule discovery from sensor time series is a challenge. Because the time series has high dimensional, numerical and continuous nature. However the general association methods can only deal with data which are symbolic and discrete. And the general association methods have high processing time consumption when the data have high dimension. So a useful framework is proposed, which is pre-processing,...
Focusing on the defects existing in classical Association Rules Mining algorithms, we explore a new way of mining temporal association rules with the time-property in dataset. By combining with the Rough Set Theory and parallel computing technology, a temporal association rules mining algorithm based on attribute reduction — ARTAR is put forward to deal with high dimensional data. The Attributes Reduction...
The key elements for understanding power consumption of a typical home are related to the activities that users are performing, the time at which appliances are used, and the interdependencies with other appliances that may be used concurrently. This information can be extracted from context rich smart meters big data. However, the main challenge is how to mine complex interdependencies among different...
The success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments, brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, "Defense...
High Utility Itemsets(HUI) Mining, instead of Frequent Pattern Mining (FIM), has been an attractive theme in data mining domain for over a decade since it can be regarded as an alternative way for researchers to identify actionable patterns. In addition, the necessity of decision-making actions and behavior-oriented strategies based on large amount of informative data impels the significance of discovering...
Due to the network alarm data in cloud environment has the characteristics of massive, redundancy, relevance, etc., traditional FP-Growth algorithm has memory and computing time double bottleneck. Therefore, this paper presents an improved FP-Growth algorithm, which based on sharing path. It scans the cube instead of multiple scans of the entire database, adopting structured storage of alarm data...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.