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Choosing and implementing technologies to extract value from big data are constant challenges for business and governments alike. This paper describes the design and implementation of a data mining tool to analyze the XML data of the U.S. university campus crimes. The main aim of this tool is to extract data stored in XML documents and to provide summarized information that can help students in determining...
Association rule mining (ARM) techniques are effective in extracting frequent patterns and hidden associations among data items in various databases. These techniques are widely used for learning behavior, predicting events and making decisions at various levels. The conventional ARM techniques are however limited to databases comprising categorical data only whereas the real-world databases mostly...
In recent innovative trends, one of the most notable features is technological convergence. The technological convergence of information technology (IT) and biotechnology (BT) is important for our social, science, and lives. However, many studies focused on the convergence of IT and BT only from the standpoint of two technologies, such studies may miss some interesting information. To address this...
Since customers' behaviors may change over time in real applications, algorithms that can be utilized to mine these drift patterns are needed. In this paper, we propose a GA-based approach for mining fuzzy concept-drift patterns. It consists of two phases. The first phase mines membership functions and the second one finds fuzzy concept-drift patterns. In the first phase, appropriate membership functions...
In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information...
Fingerprint recognition for Gender classification method done through various techniques like Support Vector Machines (SVM), Neural Network (NN), Fuzzy- C Means (FCM). This study highlights the various ridge related methods like fingerprint ridge count, ridge density, ridge thickness to valley thickness ration, ridge width and fingerprint patterns used for gender identification.[4] This paper presents...
Retail marketers are constantly looking for ways to improve the effectiveness of their campaigns. One way to do this is to target customers with the particular offers most likely to attract them back to the store and to spend more time and money on their next visit. Demographic market segmentation is an approach to segmenting markets. A company divides the larger market into groups based on several...
XML actually developed as a benchmark for caching, dispense and interchanging data over multiple platforms. The XML data is on the grow over the time in fast rate. Enterprises want formulating queries on XML datasets habitually. As giant XML data is retrievable, it is not easy job to pull out vital data from XML dataset. It is computationally expensive to answer queries without any sustain. Towards...
In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using artificial bee colony algorithm (ABC). The Artificial bee colony algorithm is an optimization algorithm based on the foraging...
The field of privacy pursues rapid advances in recent years because of the increases in the ability to store data. One of the most important topics in research community is Privacy preserving data mining (PPDM). Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. People today have become well aware of the privacy...
This article is about next generation knowledge management technologies. The original approach was to externalize the tacit knowledge of human beings. Store the externalized content, mainly text or data based documents, later multimedia contents. Handle these documents in different data bases, together with their metadata. The new associative data models will be able to handle the stored content units...
Nowadays, high volumes of valuable uncertain data can be easily collected or generated at high velocity in many real-life applications. Mining these uncertain Big data is computationally intensive due to the presence of existential probability values associated with items in every transaction in the uncertain data. Each existential probability value expresses the likelihood of that item to be present...
Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules of the domain. Some of these approaches use the user's knowledge to guide the exploration...
Depression a latest epidemic of modern era has always drawn the attention of researcher's to find & evaluate the level, causes & prevention. According to psychiatrists it's not a psychological disorder but it creates the stimulation & simulation of co-ordination failure. The worst case of the leading depression level may contemplates a person to attempt suicide, loss of energy,...
Extraction of interesting negative association rules from large data sets is measured as a key feature of data mining. Many researchers discovered numerous algorithms and methods to find out negative and positive association rules. From the existing approaches, the frequent pattern growth (FP-Growth) approach is well-organized and capable method for finding the item sets which are frequent, without...
The purpose of Text Mining is to process unstructured (textual) information, extracting meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining (statistical and machine learning) algorithms. Information can be extracted to derive summaries for the words contained in the documents or to compute summaries for the documents...
Fault management is an important part of network management, it is responsible for fault detection and locating the fault in the network. The modern network system, distributing widely, has a huge, complicated structure. Even if there is a small fault, it would bring users huge economic losses, However, in the actual network, a network fault usually leads to a number of alarm events, so we must analysis...
Discovering the hidden knowledge from large volume of educational data and applying it properly for decision making is essential for ensuring high quality education in any academic institution. This knowledge is extractable through data mining techniques. Association Rule Mining technique aims at discovering implicative tendencies that can provide valuable information for the decision maker. In this...
This paper describes the design and development of a location-based mobile shopping application for bakery product shops. Whole application is deployed on cloud. The three-tier architecture consists of, front-end, middle-ware and back-end. The front-end level is a location-based mobile shopping application for android mobile devices, for purchasing bakery products of nearby places. Front-end level...
Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme -- APM...
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