Data Mining - Concepts and Techniques.

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

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Communication without words

Food and nutrition are a key to have good health. They are important for everyone to maintain a healthy diet especially for diabetic patients who have several limitations. Nutrition therapy is a major solution to prevent, manage and control diabetes by managing the nutrition based on the belief that food provides vital medicine and maintains a good health. Typically, diabetic patients need to avoid additional sugar and fat so the food pyramid is recommended to the patients for finding the substitution from the same food group. However, there is still a dietary diversity within food groups that can affect the diabetic patients. In this study, we proposed Food Recommendation System (FRS) by using food clustering analysis for diabetic patients. Our system will recommend the proper substituted foods in the context of nutrition and food characteristic. We used Self-Organizing Map (SOM) and K-mean clustering for food clustering analysis which is based on the similarity of eight significant nutrients for diabetic patient. At the end, the FRS was evaluated by nutritionists and it has performed very well and useful for nutrition area.

Food Recommendation System Using Clustering Analysis for Diabetic Patients

Combining new Fast Opposite Gradient Search with Ant Colony Optimization for solving travelling salesman problem

Face detection and facial feature localization without considering the appearance of image context

Nowadays, the Internet is now becoming a part of our everyday lives. Many services, including Email, search engine, and web board on Internet, are provided with free of charge and unintentionally turns them into vulnerability services. Many software robots or, in short term, bots are developed with purpose to use such services illegally and automatically. Thus, web sites employ human authentication mechanism called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) to counter this attack. Unfortunately, many CAPTCHA have been already broken by bots and some CAPTCHA are difficult to read by human. In this paper, a new CAPTCHA method called 3D CAPTCHA is proposed to provide an enhanced protection from bots. This method based on assumption that human can recognize 3D character image better than Optical Character Recognition (OCR) software bots.

3D CAPTCHA: A Next Generation of the CAPTCHA

This paper proposes a very fast 1-pass-throw-away learning algorithm based on a hyperellipsoidal function that can be translated and rotated to cover the data set during learning process. The translation and rotation of hyperellipsoidal function depends upon the distribution of the data set. In addition, we present versatile elliptic basis function (VEBF) neural network with one hidden layer. The hidden layer is adaptively divided into subhidden layers according to the number of classes of the training data set. Each subhidden layer can be scaled by incrementing a new node to learn new samples during training process. The learning time is O(n), where n is the number of data. The network can independently learn any new incoming datum without involving the previously learned data. There is no need to store all the data in order to mix with the new incoming data during the learning process.

Suphakant Phimoltares

Papers

Food Recommendation System Using Clustering Analysis for Diabetic Patients

Combining new Fast Opposite Gradient Search with Ant Colony Optimization for solving travelling salesman problem

Face detection and facial feature localization without considering the appearance of image context

3D CAPTCHA: A Next Generation of the CAPTCHA

A Very Fast Neural Learning for Classification Using Only New Incoming Datum