Data Mining - Concepts and Techniques.

Pattern Identification on various domains have become one of the most researched fields. Accuracy of all traditional and standard classifiers is highly proportional to the completeness or quality of the training data. Completeness is bound by various parameters such as noise, highly representative samples of the real world population, availability of training data, dimensionality etc. Another very pressing and domineering issue identified in real world data sets is that the data is well-dominated by typical occurring examples but with only a few rare or unusual occurrences. This distribution among classes make the real world data inherently imbalanced in many domains like medicine, finance, marketing, web, fault detection, anomaly detection etc. This chapter aims to highlight the existence of imbalance in all real world data and the need to focus on the inherent characteristics present in imbalanced data that can degrade the performance of classifiers. It provides an overview of the existing effective methods and solutions implemented towards the significant problems of imbalanced data for improvement in the performance of standard classifiers. Efficient metrics for evaluating the performance of imbalanced learning models followed by future directions for research is been highlighted.

Learning From Imbalanced Data

Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract useful pattern from these complex data sources several popular spatial data clustering techniques have been proposed. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a pioneer density based algorithm. It can discover clusters of any arbitrary shape and size in databases containing even noise and outliers. DBSCAN however are known to have a number of problems such as: (a) it requires user's input to specify parameter values for executing the algorithm; (b) it is prone to dilemma in deciding meaningful clusters from datasets with varying densities; (c) and it incurs certain computational complexity. Many researchers attempted to enhance the basic DBSCAN algorithm, in order to overcome these drawbacks, such as VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN. In this study, we survey over different variations of DBSCAN algorithms that were proposed so far. These variations are critically evaluated and their limitations are also listed.

DBSCAN: Past, present and future

A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method

Proceedings of the twenty-first international conference on Machine learning

Rough-DBSCAN: A fast hybrid density based clustering method for large data sets

Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets.

l-DBSCAN : A Fast Hybrid Density Based Clustering Method

Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning.

/pdf/semi-supervised-learning-a-brief-review-34s96mslfc.pdf

Semi-supervised learning: a brief review

https://ccc.inaoep.mx/~ariel/2012/A%20distance%20based%20clustering%20method%20for%20arbitrary%20shaped%20clusters%20in%20large%20datasets.pdf

A distance based clustering method for arbitrary shaped clusters in large datasets

k-means clustering method is an iterative partition-based method which for finite data-sets converges to a solution in a finite time. The running time of this method grows linearly with respect to the size of the data-set. Many variants have been proposed to speed-up the conventional k-means clustering method. In this paper, we propose a prototype-based hybrid approach to speed-up the k-means clustering method. The proposed method, first partitions the data-set into small clusters (grouplets), which are of varying sizes. Each grouplet is represented by a prototype. Later, the set of prototypes is partitioned into k clusters using the modified k-means method. The modified k-means clustering method is similar to the conventional k-means method but it avoids empty clusters (the clusters to which no pattern is assigned) in the iterative process. In each cluster of prototypes, each prototype is replaced by its corresponding set of patterns (which formed the grouplet) to derive a partition of the data-set. Since this partition of the data-set can deviate from the partition obtained using the conventional k-means method over the entire data-set, a correcting step is proposed. Both theoretically and experimentally, the conventional k-means method and the proposed hybrid method (augmented with the correcting step) are shown to yield the same result (provided, the initial k seed points are same). But, the proposed method is much faster than the conventional one. Experimentally, the proposed method is compared with the conventional method and the other recent methods that are proposed to speed-up the k-means method.

P. Viswanath

Papers

Rough-DBSCAN: A fast hybrid density based clustering method for large data sets

l-DBSCAN : A Fast Hybrid Density Based Clustering Method

Semi-supervised learning: a brief review

A distance based clustering method for arbitrary shaped clusters in large datasets

A hybrid approach to speed-up the k-means clustering method