Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...

Mining fuzzy rules is one of the best ways to summarize large databases while keeping information as clear and understandable as possible for the end-user. Several approaches have been proposed to mine such fuzzy rules, in particular to mine fuzzy association rules. However, we argue that it is important to mine rules that convey information about the order. For instance, it is very interesting...

Association rule mining is the most popular technique in the area of data mining. The main task of this technique is to find the frequent patterns by using minimum support thresholds decided by the user. The Apriori algorithm is a classical algorithm among association rule mining techniques. This algorithm is inefficient because it scans the database many times. Second, if the database is large...

Finding multilevel association rules in transaction databases is most commonly seen in is widely used in data mining. In this paper, we present a model of mining multilevel association rules which satisfies the different minimum support at each level, we have employed fuzzy set concepts, multi-level taxonomy and different minimum supports to find fuzzy multilevel association rules in a given tr...

요 약 Intelligent and adaptive services for mobile users are researchable topics nowadays to be able to provide the mobile user an immediate knowledge of the location. This research work presents a framework for collaborative location-based services and uses data mining approach based on neuro-fuzzy system. The proposed framework supports data mining for knowledge discovery to location informatio...

This paper describes a prototype intelligent intrusion detection system (IIDS) that is being developed to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic. This system combines two distinct intrusion detection approaches: 1) anomaly based intrusion detection using fuzzy data mining techniques, and 2) misuse detection using traditional rule-based expert system tec...

The research area of Data Mining or Knowledge Discovery in Databases has emerged in response to the challenges of analyzing the tremendously growing datasets gathered nowadays by companies and research institutions. Classification is one important task of data mining, where fuzzy techniques to extract classification rules from data are appealing due to their human understandable modeling. Often...

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In the past, the minimum supports and minimum confidences were set at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural and understandable for human beings. This paper thus attempts to propose a new mining approac...

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert’s knowledge, it calls a data mining function, a genetic algorithm,...

Social media sites are now very popular medium for showing your views and opinions to others with a great amount of various types of information uploaded by the social media users, a social web page can be a collection of pages, audio files, photographs, images, video files and other forms of data in structured or unstructured form. It is also huge, diverse, and dynamic, hence raises the scalab...