Clustering Or Cluster Analysis Is Defined As The Process Of Organizing Objects

(2011) presented research of a clustering issue for office workplaces using a fuzzy bat algorithm. Khan and Sahari (2012a) as well presented a comparison study of bat algorithm with PSO, GA, along with other algorithms in the perspective for e-learning, and thus recommended that bat algorithm has clearly some advantages over other algorithms. Then, they (Khan and Sahari, 2012b) also suggested a study of clustering problems using bat algorithm and its expansion like a bi-sonar optimization variant with positive results. On the other side, Mishra et al. (2012) applied bat algorithm to categorize microarray data, while Natarajan et al. (2012) presented a comparison study of cuckoo search and also bat algorithm for Bloom filter optimization. Damodaram and Valarmathi (2012) studied phishing website detection applying modified bat algorithm and also attained very good outcome.

Hierarchal classification-The organization of objects into classes and subclasses on the basis of similarities and differences.

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

• Clustering: Hierarchical clustering groups patterns into a set of clusters which are in the form of a hierarchy.

Segmentation is essentially the identification of subsets of buyers within a market who share similar needs and who demonstrate similar buyer behavior. Segmentation aims to match groups of purchasers with the same set of needs and buyer behavior. Such a group is known as a 'segment '.

The important aim of image segmentation is the domain independent partitioning of an image into a set of disjoint regions that can be visually differentiated, homogeneous and meaningful with respect to some characteristics or computed property such as grey level, texture or colour to enable easy image analysis. Image segmentation is a useful tool in many realms including industry, health care, astronomy, and various other fields. Segmentation in concept is a very simple idea. Simply looking at an image, one can tell what regions are contained in a picture. Is it a building, a person, a cell, or just simply background? Visually it is very easy to determine what a region of interest is and what is not. Doing so with a computer algorithm on the other hand is not so easy. How do you determine what defines a region? What features distinguish one region from another? What determines how many regions you have in a given image? Discontinuity and similarity/homogeneity are two basic properties of the pixels in relation to their local neighbourhood used in many segmentation methods. The segmentation methods that are based on discontinuity property of pixels are considered as boundary or edges based techniques and that are based on similarity or homogeneity are region based techniques.

* Clustering analyzes data objects without consulting a known class label. The objects are clustered or grouped based on the principle of

Clustering is a process of grouping objects with some similar properties. Any cluster should exhibit fundamental properties, low between class comparability and similarity. Clustering is an unsupervised learning i.e. it adapts by perception instead of illustrations. There is no predefined class conditions exist for

Secondly, Omran propose Dynamic Clustering (DCPSO) algorithm based on binary PSO combining with k-means clustering. In this approach, PSO is used for clustering the data while k-means is used to refine the clustering solution. At first, the number of clusters is determined automatically and the data sets are clustered based on minimal user interference. In order to decrease the effects of initial conditions, a relatively large number of clusters are generated firstly. Then the number of clusters is optimized by binary particle swarm optimization, while K-means clustering algorithm is used to select the centroids. Both synthetic and natural images are used to test the approach, which show that the optimum number of clusters are generally founded on the tested images.

Various classification methods have been developed to extract information from imageries. The two main types are pixel and object-based methods. Pixel-based methods can be cluster based unsupervised or supervised classification whereas the later uses statistical (e.g., maximum likelihood algorism) and non-statistical algorithms (e.g., support vector machines) (Lu, Li, Kuang, & Moran, 2014). The object-based classification which overcomes some of the particular problems encountered with pixel-based classification (Blaschke, 2010) was used to analyze

As Big Data is referring to terabytes of data and clustering algorithms come with high computational costs, the challenge is how to cope with this phenomenon in order to organize clustering techniques to big data for obtaining the results in a reasonable time (Li et al., 2015). This report surveys the trend and progress of clustering algorithms to handle big data and overcome the challenges. The algorithms and the targeted challenges for developing improved clustering techniques are presented and evaluated. Moreover, a great attention is paid to the K-Means technique and its revisions, as it remains one of the widespread methods.

Abstract— As there is vivid implementations in the multimedia technologies, users find it complex for retrieving information with traditional image retrieval techniques. The CBIR techniques are becoming an efficient techniques for exact and fast retrieval of images. CBIR is the technique which uses visual features of image such as shape, color, texture etc, to search the image based on the user requirements from large database according to the user request in the form of a query. In this paper various techniques of CBIR such as k-means clustering, k-nearest neighbors Algorithm (KNN), color structure descriptor (CSD), Text based image retrieval (TBIR) techniques which increase the effectiveness of fast retrieval are discussed and analyzed.

The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest center. When no point is pending, the first step is completed and an early group age is done. At this point we need to re-calculate k new centroids as barycenter of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new center. A loop has been generated. As a result of this loop we may notice that the k centers change their location step by step until no more changes are done or in other words centers do not move any more. Finally, this algorithm aims at minimizing

After the image has been processed using the 3 frameworks proposed the image needs to be classified which is done using the image classification technique of image mining. Classification can be carried out by applying the method of supervised classification and unsupervised classification but here we will mainly focus upon supervised classification.

Segmenting or dividing a digital image into region of interests or meaningful structures in general plays a momentous role in quite a few image processing tasks. Image analysis, image visualization, object representation are some of them. The prime objective of segmenting a digital image is to change its representation so that it looks more expressive for image analysis. During the course of action in image segmentation, each and every pixel of the image segmentation is assigned a label or value. The pixels that share the same value also share homogeneous traits. The examples can include color, texture, intensity or some other features. Image segmentation can be defined as the technique to divide the an image f (x, y) into a non empty subset f1, f2, ...., fn which is continuous and disconnected. This step contributes in feature extraction. There are quite a few applications where image segmentation plays a pivotal role. These applications vary from image filtering, face recognition, medical imaging