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Frequent pattern mining plays an essential role in association rule mining, which has been a focused theme in data mining research for over the past 15 years. Since the pioneering work of Agrawal, abundant literature has been dedicated to this research. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions.
Traffic classification has become a crucial domain of research due to the rise in applications that are either encrypted or tend to change port consecutively. The challenge of flow classification is to determine the applications involved without any information on the payload. In this paper, our goal is to achieve a robust and reliable flow classification using data mining techniques. We propose a...
Dendrogram for clustering retail items is a tool used in customer relationship management (CRM) for implementing homogeneous schemes for all the items in one cluster. In agglomerative hierarchical clustering, dendrograms are developed based on the concept of `distance' between the entities or, groups of entities. These entities may be the customers, retail items, business units etc. as per the business...
Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet...
As time advances new transactions are added to the databases. The extensive amounts of knowledge and data stored in databases require the development of specialized tools for storing and accessing of data, data analysis and effective use of stored knowledge of data. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing...
An important problem that arises during the data mining process in many new emerging application domains is mining data with temporal dependencies. One such application domain is activity discovery and recognition. Activity discovery and recognition is used in many real world systems, such as assisted living and security systems, and it has been vastly studied in recent years. However, the temporal...
Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In...
The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with...
With development of internet and database technology, web mining has got more and more attentions from information science domain. This paper proposes a document clustering technique based on term clustering and association rules. In this technique, extract words from document collection firstly, then construct term clustering according to AMI(Average Mutual Information) between terms, document VSM(Vector...
In this paper, an enhanced efficient approach for speeding up the evolution process for finding minimum supports, membership functions and fuzzy association rules is proposed by utilizing clustering techniques. All the chromosomes use the requirement satisfaction derived only from the representative chromosomes in the clusters and from their own suitability of membership functions to calculate the...
In this study, we present a novel approach to recommend the personalized book lists for the university members. Our approach consists of clustering the university members into different clusters based on their recent circulation activities and discovering the interest patterns of members in the cluster. In the first step, we clustered members sharing the common interests to the same cluster by using...
Detailed inspection of transactional data can reveal various useful information, in which of special importance are relationships between transaction elements. Hierarchical clustering coupled with specific distance measures reveal those relationships from one angle. Additionally, association rules - a natural method of inspecting transactional data - is able to reveal relationships between each pair...
The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of...
Information retrieval is the most popular database technology, which is focusing on data analysis, association rules, pattern discovery and so on. It is critical to find efficient ways of mining large data sets. In this paper we present a Personalized Travel Information System, called PTIS. PTIS can automatically link to the travel sites to collect information, and then create new data rules. According...
Valuable information can be hidden in images. The need for image mining is high in view of the fast growing amounts of image data. In this paper, we first point out unique characteristics of image mining, then analyze the overall process and discuss the main technology of image mining, namely, image classification and clustering, association rule mining. Some applications in various areas are introduced...
According to the existing mining algorithm of fuzzy association rules, a novel fuzzy positive and negative association rules algorithm will be proposed in this paper. We focus on the membership function of fuzzy set and minimum support parameters of positive and negative association rules and adopt a method that selects parameters automatically which is based on the k-means clustering. Besides, multi-level...
On mining quantitative association rules and the segmentation of numerical attributes variables of ordered data, Fisher cluster method can be used to determine the segmentation range and the segmentation number of this variable value. The method takes the data interval and data density into account. Therefore it is of great significance to data pre-processing.
Many association rule mining algorithms have been developed to extract interesting patterns from large databases. However, a large amount of knowledge explicitly represented in domain knowledge has not been used to reduce the number of association rules. A significant number of known associations are unnecessarily extracted by association rule mining algorithms. The result is the generation of hundreds...
Multimedia Mining is a young but challenging subfield in Data Mining. There are not unified conclusions in the concept, content and methods of Multimedia Mining, Multimedia mining architecture and framework has to be further studied. Based on these, it introduces a appropriate data mining model based on multimedia database; it has analyzed the methods which are associated with the model: data cube,...
Presently, in the data mining scenario clustering of large dataset is one of the very important techniques widely applied to many applications including social network analysis. Applying more specific pre-processing method to prepare the data for clustering algorithms is considered to be a significant step for generating meaningful segments. In this paper we propose an innovative clustering technique...
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