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Using solely the information retrieved by audio finger-printing techniques, we propose methods to treat a possibly large dataset of user-generated audio content, that (1) enable the grouping of several audio files that contain a common audio excerpt (i.e. are relative to the same event), and (2) give information about how those files are correlated in terms of time and quality inside each event. Furthermore,...
Learning Management Systems such as Modular Object-Oriented Dynamic Learning Environment (Moodle) only supports random group assignment or instructor based assignment method. However, with the understanding that random assignment method only increases the likelihood of heterogeneity in the group, while instructor based method involves the instructors and it is not dynamic, there is need to develop...
Due to the emerging Big Data paradigm, traditional data management techniques result inadequate in many real life scenarios. In particular, the availability of huge amounts of data pertaining to social interactions among users calls for advanced analysis strategies. Furthermore, heterogeneity and high speed of this data require suitable data storage and management tools to be designed from scratch...
WiFi Fingerprint Positioning (WFP) in outdoor scenario needs mass location information including WiFi signal map and GPS (Global Positioning System) information. Generally pre-measured solution can provide high quality data but it needs lots of labor and time. Different from pre-measured solution, crowdsourcing is an economic and efficient way to obtain location information. WFP based on Clustering...
The increase of the quantity of user-generated content experienced in social media has boosted the importance of analysing and organising the content by its quality. Here, we propose a method that uses audio fingerprinting to organise and infer the quality of user-generated audio content. The proposed method detects the overlapping segments between different audio clips to organise and cluster the...
In order to extract useful information from massive data, the researchers proposed data mining technology, one of the most critical technology is clustering analysis technology. In this paper, an improved clustering algorithm based on shared nearest neighbor is proposed for the existing shared clustering algorithm, and the improved algorithm is applied to fingerprint localization. The algorithm reduces...
Cluster analysis aims at classifying data elements into different categories according to their similarity. It is a common task in data mining and useful in various field including pattern recognition, machine learning, information retrieval and so on. As an extensive studied area, many clustering methods are proposed in literature. Among them, some methods are focused on mining clusters with arbitrary...
Designing Chinese-Uighur-English online dictionary is very important for the development of ethnic scientific research and education, which is the basis for the work of Uighur semantic study. Online dictionary with a huge thesaurus can be implemented by knowledge graph, existing works have not addressed in much detail. In order to design the thesaurus of the online dictionary, this paper is based...
This paper proposes an attack pattern mining algorithm to extract attack pattern in massive security logs. The improved fuzzy clustering algorithm is used to generate sequence set. Then PrefixSpan is used to mine frequent sequence from the sequence set. The experimental results show that this algorithm can effectively mine the attack pattern, improve the accuracy and generate more valuable attack...
There is no previous research that compares the results of k-means, CLOPE clustering and Latent Dirichlet Allocation (LDA) topic modeling algorithms for detecting trending topics on tweets. Since not all tweets contain hashtags, we considered three training data feature sets: hashtags, keywords and keywords + hashtags in this study. Our proposed methodology proved that CLOPE can also be used in a...
Data analysis plays an indispensable role in the knowledge discovery process of extracting of interesting patterns or knowledge for understanding various phenomena or wide applications. Visual Data Mining is further presenting implicit but useful knowledge from large data sets using visualization techniques, to create visual images which aid in the understanding of complex, often massive representations...
In this digital world, we are facing the flood of data, but depriving for knowledge. The eminent need of mining is useful to extract the hidden pattern from the wide availability of vast amount of data. Clustering is one such useful mining tool to handle this unfavorable situation by carrying out crucial steps refers as cluster analysis. It is the process of a grouping of patterns into clusters based...
In many organizations huge amount of data is generated. Organizations use this data for their own benefit. Data mining extracts useful knowledge from huge data. Association rule mining is a powerful technique to find hidden patterns in large database. The limitation of mining association rules is that some sensitive patterns are revealed from sensitive rules. It is necessary to hide sensitive rules...
The criminal behavior is a disorderliness that is a combined result of social and economic aspects. The crime rate has expanded and the activities of criminals have broaden in last few decades due to better communication system and transport. Crimes cause terror and damage our community enormously in several means. In cities and towns the crime trends rises due to fast developmental activities and...
The Internet provides an excellent extent of useful information that is sometimes arranged for its users, that makes it difficult to extract relevant information from various sources. So that, this paper proposes a hybrid Artificial Bee Colony and Improved K-means bunch algorithmic program provides all types data of data repository and has been terribly successful in dispersive information to users...
In this researched paper, a clustering algorithm to discover clusters of unusual shapes and densities. Hierarchical and Density based ways are implemented for constructing minimum Spanning Tree; the MST can be divided into two segments. In the first segment, local density is guesstimate at every data point. In the subsequent segment, hierarchical ways are used by combining clusters according to the...
Customer Relationship Management (CRM) is an overall process of building and retaining profitable customers with an organization and directed towards improving business relationship with customers. With analysis of customer data in the CRM database helps to create new approach to lead the business strategies. Analytical CRM helps to analyze customer data and interactions through various data mining...
Gathering the most relevant data for one's need, from the huge collection of data in the internet is a work of great difficult. To make it easier, we propose an application called text clustering, which is an automatic grouping of text documents into clusters, so that documents within a cluster defines the similarity between them, but they are not similar to documents in other clusters. Most of existing...
To retrain an existing multilayer perceptron (MLP) on-line using newly observed data, it is necessary to incorporate the new information while preserving the performance of the network. This is known as the “plasticitystability” problem. For this purpose, we proposed an algorithm for on-line training with guide data (OLTA-GD). OLTA-GD is good for implementation in portable/wearable computing devices...
Mortality analytics is an emerging research area that discovers and communicates meaningful patterns in clinical data to reduce mortality rates. Nonetheless, intensive care unit (ICU) mortality analytics for leading causes, such as circulatory system diseases (CDS), is still complicated due to the interactions of different mortality causes. To improve analytics accuracy and quality, clustering analysis...
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