Adapting autonomously classification data mining algorithms for ubiquitous devices

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

Technological advancements such as ubiquitous communication, pervasive computing and ambient intelligence, have made context-aware services a reality. The health sector has adopted the use of context-aware e-health services to improve the quality of life, provide real-time patient monitoring, and in general better healthcare. However, the capability of accessing patients’ context and health information via context-aware e-health services has given rise to issues of privacy and trust. The main objective of this research is to propose a Trust Model, which ensures that the privacy of the patient’s health and context information is maintained and secure from unauthorised access. This means that the patient cannot only trust the service and context-providers, but also has control of their context and health information. The proposed trust model monitors the patient’s health status and context information using sensors. The model is implemented using a proof-of-concept context-aware e-health service that uses blockchain technology to ensure data integrity and accountability. If successfully implemented, the security of the user’s context and health information will be guaranteed, and thus lead to increased uptake of context-aware e-health services.

Heart diseases are one of the crucial diseases that may cause fatality in both men and women. About 12 million deaths occur across the world due to heart diseases. With the advancement in information technology, it is possible for the Healthcare industry to store enormous volume of data containing millions of patient’s medical information along with their treatment details. If utilized in an efficient manner, this information helps the doctors to diagnose the diseases in a precise manner. Data mining algorithms are employed to analyse huge data sets and to discover unseen patterns. Data mining plays an essential role in medical diagnosis. Doctors bank on different computer models which uses data mining algorithms to prefigure different kinds of diseases in patients. So, the need is to design a methodical data mining algorithm that helps for better forecast of diseases. The main goal of this work is to create an ensemble of algorithms which results in better accuracy. The ensemble is constructed by making use of stacking ensemble technique, which comprises of two categorization algorithms namely Naïve Bayes and Artificial Neural Network. The Cleveland heart disease data set acquired from UCI machine learning repository containing 14 attributes and 303 instances is given as input to these algorithms. From our experimental analysis it is evident that the proposed ensemble scheme results in a better accuracy.

This paper describes an approach to identify the state of a power system using Data Mining Algorithms. With the advent of Phasor Measurement Units (PMU), a huge amount of power system data is being stored day-to-day in the Phasor Data Collectors (PDCs). Knowledge discovery and machine learning techniques can make use of this data to extract valuable information and interesting patterns in these databases. In this paper, Symbolic Aggregate ApproXimation (SAX) is used to convert the time stamped phasor data from the PMUs into symbolic strings and Data Mining (DM) algorithms are used to predict the current state of a power system. Along with the state, the location and cause of disturbance is also identified in minimal time. The effectiveness of different DM algorithms for determining state of a power system is also shown using the measures of accuracy, precision and recall.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

The recent advances in technologies for collecting spatial and spatio-temporal data (e.g. Cellular Phones, GPS devices) has facilitated the collection of data referenced in space and time. These huge collections of data often hide interesting information which conventional systems are unable to discover. Spatial and spatio-temporal data require complex data preprocessing, transformation, data mining, and post-processing techniques to extract novel, useful, and understandable patterns. The importance of spatial data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, repositories of remote-sensing images, trajectories of moving objects generated by mobile devices, etc. Applications include Mobile-commerce industry (location-based services), climatological effects of El Nino, land-use classification and global change using satellite imagery, finding crime hot spots, and local instability in traffic. The main goal of this tutorial is to disseminate this research field in Brazil, giving an overview of the current state of the art and the main methodologies and algorithms for spatial and spatiotemporal data mining.

Context awareness, user friendliness, and interactivity are very important and powerful concepts in building useful teaching/learning environments for the benefit of mankind. Advancements in technology are enabling us to look at new use cases in the methods of teaching, information sharing, knowledge dissemination, and self-learning. Learning environments are not only for teaching things to people., but also for providing more information to them on any subject that they may require from time to time. User friendliness can be enhanced with clever use of sensors to give hands free interactivity with the devices/gadgets. Context aware applications require sensors to get the context information. Mobile devices like smart phones and tablets today come with a hand full of sensors mounted on them which include one or two cameras, a microphone, a touch screen, an accelerometer to sense motion, and in addition have GPS and digital compass to provide location information to applications requiring these sensory inputs. The computational power packed in the processors used in these devices/gadgets including special support for graphics processing, the near general purpose computer like operating system environments, and the quality of cameras available, are all enabling application developers to map some serious computer vision based applications on today's mobile devices. This reality has encouraged us to get into development of Augmented Reality (AR) based interactive learning environments on mobile devices, leading to our development of a software framework for AR applications development and also development of an AR book and an AR board. AR has been called the eighth mass medium, after print, recordings, cinema, radio, television, Internet and mobile phones. There is ample scope for enhancing the learning/teaching experiences of the users with AR based learning environments. AR makes learning more effective and an enjoyable experience by providing more realistic information with the use of 3D graphics and animated models.

SummaryOne of the most important challenges of African countries is the effective use of information and communication technologies and its generalization in the different sectors (education, economic, political, and so on). This will have a great impact on different aspects of society and economic activities by making everyday procedures easier and more efficient. In this same context, we are interested in this article by the proposition of a decision support framework based on the use of data mining (DM) techniques. Using DM in e‐government is the process of translating data to appropriate knowledge which can be useful for decision‐making. We propose a framework whose goal is the generation of association rules (ARs) for better decision‐making. This framework includes two approaches: (1) the first approach applies different DM algorithms and (2) the second approach optimizes the first one by considering two different metaheuristics: the Genetic algorithm and the Cuckoo search algorithm. A new relevance measure called “Weighted Dominance” has been considered to evaluate the quality of the generated ARs. Extensive experiments have been conducted using different datasets. The results obtained demonstrated the effectiveness of combining DM and optimization algorithms compared to the first approach. Finally, a case study related to an Algerian mobile phone company has been presented illustrating the use of our framework in the decision‐making process.

Use of machine learning (ML) and data mining (DM) algorithms has surfaced more often in the recent years for disease risk prediction problems in the healthcare communities. Several traditional feature selection models are combined with the DM and ML algorithms to improve accuracy of the disease risk prediction. In this study, a new Bio-inspired Ensemble Feature Selection (BEFS) model is introduced which is applied with the DM and ML algorithms. In the BEFS model, the most relevant and highly contributing features in the prediction are determined with a bio-inspired algorithm i.e., genetic algorithm, and an ensemble algorithm i.e., random forest algorithm. These important features obtained from the proposed model are then combined in various combinations and applied with the DM and ML algorithms, here logistic regression (LR) and random forest (RF), and the results obtained are promising. The experiment is executed using the famous ML language R. To accomplish this objective, the Breast Cancer Wisconsin (Diagnostic) dataset of UCI (University of California, Irvine) ML repository is utilized. In the experimental outcomes, the highest accuracy attained with the BEFS model is 96.49%, the AUC (Area Under Curve) achieved is 96%, and the sensitivity is 98.11%. These results, which greatly improve the disease risk prediction, are higher than several other existing works, while utilizing only six most relevant features out of the thirty two features of the dataset.

This paper presents a hybrid approach that integrates a genetic algorithm (GA) and data mining to produce control signatures. The control signatures define the best parameter intervals leading to a desired outcome. This hybrid method integrates multiple rule sets generated by a data mining algorithm with the fitness function of a GA. The solutions of the GA represent intersections among rules providing tight parameter bounds. The integration of intuitive rules provides an explanation for each generated control setting and it provides insights into the decision making process. The ability to analyze parameter trends and the feasible solutions generated by the GA with respect to the outcomes is another benefit of the proposed hybrid method. The presented approach for deriving control signatures is applicable to various domains, such as energy, medical protocols, manufacturing, airline operations, customer service, and so on. Control signatures were developed and tested for control of a power plant boiler. These signatures discovered insightful relationships among parameters. The results and benefits of the proposed method for the power plant boiler are discussed in the paper.

Data Mining algorithms and machine learning techniques form a key part of the majority of computing applications today. They are becoming an inherent part of business decision processes, e-commerce, social networking and social media applications as well as commercial and scientific computing applications. It is becoming increasingly important to provide a high performance computing platform for these emerging data mining applications. In this paper we explore the performance characteristics of the data mining benchmark suite MineBench across three “tock” generations of Intel microarchitecture. Our objective is to study the impact of microarchitecture improvements on the performance of data mining algorithms. We present comparative microarchitecture characteristics between data mining algorithms and SPEC INT 2006 benchmarks. We have proposed a generic cycle accounting methodology to attribute performance improvements to various units of the microprocessor. The proposed methodology helps differentiate the impact on performance due to front-end and back-end microarchitecture improvements.

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

The amount of data being generated and stored is growing exponentially, owed in part to the continuing advances in computer technology. These data present tremendous opportunities in data mining, a burgeoning field in computer science that focuses on the development of methods that can extract knowledge from data. In many real world problems, these data mining algorithms have access to massive amounts of data. Mining all the available data is prohibitive due to computational (time and memory) constraints. Much of the current research is concerned with scaling up data mining algorithms (i.e. improving on existing data mining algorithms for larger datasets). An alternative approach is to scale down the data. Thus, determining a smallest sufficient training set size that obtains the same accuracy as the entire available dataset remains an important research question. Our research focuses on selecting how many (sampling) instances to present to the data mining algorithm. The goals of this paper is to study and characterize the properties of learning curves, integrate them with Chebyshev Bound to come up with an efficient general purpose adaptive sampling schedule, and to empirically validate our algorithm for scaling down the data.

Understanding the Design of Mobile Social Networking

Computer-assisted data mining algorithms (DMAs) are being studied to screen spontaneous reporting databases for signals of novel adverse events. The performance characteristics and optimum deployment of these techniques remain to be established. To explore issues in the practical evaluation and deployment of DMAs by comparing findings from an empirical Bayesian DMA with those from a traditional drug safety surveillance program. Published findings from early postmarketing safety surveillance of thalidomide were compared with findings from an empirical Bayesian DMA. Differential results were used to explore practical issues in the evaluation and deployment of DMAs. Most adverse events highlighted by each method were compatible with the product labeling or natural history/complications of reported treatment indications. Traditional surveillance highlighted 4 potentially serious and unexpected adverse events (Stevens-Johnson syndrome, toxic epidermal necrolysis, seizures, skin ulcers) warranting labeling amendments or close monitoring. None of these adverse event terms generated a signal using the DMA. The DMA would not have enhanced early postmarketing surveillance in this particular setting. While the results cannot be used to draw inferences about the global performance of DMAs, they illustrate the following: (1) DMA performance may be highly situation dependent; (2) over-reliance on these methods may have deleterious consequences, especially with so-called "designated medical events"; and (3) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous factors that may influence comparative performance and incremental utility of DMAs.