Traditional association rules mining cannot meet the demands arising from some real applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. It performs very efficiently in terms of speed and memory cost both on synthetic and real databases, even on large databases that are difficult for existing algorithms to handle.

/pdf/a-two-phase-algorithm-for-fast-discovery-of-high-utility-54h1513bjw.pdf

A two-phase algorithm for fast discovery of high utility itemsets

The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still „information rich‟ but „knowledge poor‟. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today‟s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.

/pdf/predictive-data-mining-for-medical-diagnosis-an-overview-of-ybk8ywsf3j.pdf

Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree (IHUPL-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP transaction frequency tree (IHUPTF-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-transaction-weighted utilization tree (IHUPTWU-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.

Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

This invention relates to methods and apparatus for improving communications between humans and devices. The invention provides a method of modulating operation of a device, comprising: providing an attentive user interface for obtaining information about an attentive state of a user; and modulating operation of a device on the basis of the obtained information, wherein the operation that is modulated is initiated by the device. Preferably, the information about the user's attentive state is eye contact of the user with the device that is sensed by the attentive user interface.

Method and Apparatus for Communication Between Humans and Devices

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions.

Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatornal explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the "downward closure property" in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a "weighted downward closure property". A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.

/pdf/weighted-association-rule-mining-using-weighted-support-and-48jclnnrdx.pdf

Weighted Association Rule Mining using weighted support and significance framework

—

Innovative systems for user-computer interaction based on the user's eye-gaze behavior have important implications for various applications. Examples include user navigation in large images, typical of astronomy or medicine, and user selection and viewing of multiple video streams. Typically, a web environment is used for these applications. System latency must be negligible, while system obtrusiveness must be small. This paper describes the implementation and initial experimentation on such an innovative system.

https://pure.royalholloway.ac.uk/portal/services/downloadRegister/890133/Murtagh-Computer_display_control.pdf

Computer display control and interaction using eye-gaze

Frontal view gait recognition for people identification has been carried out using single RGB, stereo RGB, Kinect 1.0, and Doppler radar. However, existing methods based on these camera technologies suffer from several problems. Therefore, we propose a four-part method for frontal view gait recognition based on the fusion of multiple features acquired from a Time-of-Flight (ToF) camera. We have developed a gait data set captured by a ToF camera. The data set includes two sessions recorded seven months apart, with 46 and 33 subjects, respectively, each with six walks with five covariates. The four-part method includes: a new human silhouette extraction algorithm that reduces the multiple reflection problem experienced by ToF cameras; a frame selection method based on a new gait cycle detection algorithm; four new gait image representations; and a novel fusion classifier. Rigorous experiments are carried out to compare the proposed method with state-of-the-art methods. The results show distinct improvements over recognition rates for all covariates. The proposed method outperforms all major existing approaches for all covariates and results in 66.1% and 81.0% Rank 1 and Rank 5 recognition rates, respectively, in overall covariates, compared with a best state-of-the-art method performance of 35.7% and 57.7%.

/pdf/frontal-view-gait-recognition-with-fusion-of-depth-features-51p4cxj3ak.pdf

Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera

Pattern Classification, by Richard O. Duda, Peter E. Hart, and David G. Stork.

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of distributed processing for training and inference. We propose a cloud‐based video analytics system based on an optimally tuned convolutional neural network to classify objects from video streams. The tuning of convolutional neural network is empowered by in‐memory distributed computing. The object classification is performed by comparing the target object with the prestored trained patterns, generating a set of matching scores. The matching scores greater than an empirically determined threshold reveal the classification of the target object. The proposed system proved to be robust to classification errors with an accuracy and precision of 97% and 96%, respectively, and can be used as a general‐purpose video analytics system.

Mohsen Farid

Papers

Weighted Association Rule Mining using weighted support and significance framework

Computer display control and interaction using eye-gaze

Frontal View Gait Recognition With Fusion of Depth Features From a Time of Flight Camera

Pattern Classification, by Richard O. Duda, Peter E. Hart, and David G. Stork.

Cloud-based video analytics using convolutional neural networks