k Nearest Neighbor (KNN)

k Nearest Neighbor (KNN): introduction The necessity of data mining techniques has emerged quite immensely nowadays due to massive increase in data. Data mining is the process of extracting patterns and mining knowledge from data. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique. The model for KNN is the entire training dataset. When a prediction is required for a unseen data instance, the KNN algorithm will search through the training dataset for the k-most similar instances. The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. Nearest neighbor classifiers is a lazy learner’s method and is based on learning by analogy. It is a supervised classification technique which is used widely. Unlike the previously described methods the nearest neighbor method waits until the last minute before doing any model construction on a given tuple. In this method the training tuples are represented in N-dimensional space. When given an unknown tuple, k-nearest neighbor classifier searches the k…

Decision Trees

Decision Tree:Overview in different kinds of supervised data mining techniques the decision trees are one of the most popular classification and prediction technique. basically the training data samples are organized in form of tree data structure. where the nodes of tree shows the attributes of the data set and edges can be used for demonstrating the values of these attributes. additionally the leaf node of the tree contains the decisions of the classifier. example the decision tree as given in figure 1. Figure 1 decision tree example in the above given figure the decision tree model is demonstrated which contains decisions in terms of yes or no at the leaf nodes. similarly the humidity, outlook and wind are the attributes which are available in data set. additionally the relevant attribute attribute values that are frequently occurred during the evaluation of patterns. sometimes these trees can also used as the IF THEN ELSE rules. from the above given example a rule can be defined as: IF (Outlook = sun & Humidity = normal) then decision = yes Advantages the following are the key advantages of any decision tree: Decision tree are simple to understand and construct even after a brief exploration….

Web recommendation system

Web recommendation system: Introduction The term recommendation is used for describing the suggestions of a particular product or service. therefore the web recommendation systems are a essential part of e-commerce applications. The users who search about some kinds of product or services the recommendation systems helps them by suggesting the most appropriate product or services. In most of the cases the web based recommendation systems are developed using the web usage mining and content mining techniques.  In this context using this concept a number of applications  are created. The recommendations systems can be described in three major categories. There is an extensive class of Web applications that involve predicting user responses to options. Such a facility is called a recommendation system. However, to bring the problem into focus, two good examples of recommendation systems are [1]: Offering news articles to on-line newspaper readers, based on a prediction of reader interests. Offering customers of on-line retailer suggestions about what they might like to buy, based on their past history of purchases and/or product searches. Recommendation systems use a number of different technologies. That can be classify these systems into two broad groups Content-based systems examine properties of the items recommended. For instance, if…

Fuzzy C means

Fuzzy C-means: Overview the fuzzy c-means algorithm is one of the most popular clustering technique in data mining. that technique enable the data objects to be available in more than one cluster at the time. therefore that technique can be used for other clustering technique implementations. that is also a unsupervised technique of data mining. that technique directly input the data samples as input and produces the clusters of data according to user requirements. Functional Overview basically that technique is works on the basis of optimization of the objective function. that helps to improve the cluster membership from the different clusters available. Clustering is a mathematical tool that attempts to discover structures or certain patterns in a dataset, where the objects inside each cluster show a certain degree of similarity. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Cluster analysis is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization. It will often necessary to modify pre-processing and parameter until the result achieves the desired properties. Fuzzy C-Means Clustering Fuzzy clustering is a powerful unsupervised method for the…

K-Means Algorithm with Numerical Explanation

K-means clustering Overview in a number of classical data mining techniques the k-means algorithm is one of the most popular technique of unsupervised learning or clustering. that technique can be used with any kind of mining techniques web mining, text mining or any structured data mining. that algorithm is suitable to used for partitioning of data into K number of clusters. therefore that technique is also known as partition based clustering approach. Basic Functioning that technique traditionally need to select k number of initial centroids. these centroids are compared with the remaining data objects in the input data samples. in order to compare the data objects the distance functions are used such as euclidean distance. Basics of clustering Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. This clustering analysis allows an object not to be part of a cluster, or strictly belong to it, calling this type of grouping hard partitioning. In the other hand, soft partitioning states that every object belongs to a cluster in a determined…

Classification and prediction in data mining

Classification is a technique of supervised learning in data mining. that technique is applied when the data patterns or samples are having some predefined pattern labels or class labels. the supervised learning algorithms first prepare the data models based on the existing patterns. these existing patterns are known as training samples. additionally the preparation of data models are known as the training of algorithms. after the training of algorithms the data model is used to recognize the similar newly appeared samples or patterns. that is a very essential and popular technique in data mining because for obtaining the precise outcomes these techniques are used. figure 1 classification There are two forms of data analysis that can be used for extract models describing important classes or predict future data trends. These two forms are as follows Classification Prediction These data analysis help us to provide a better understanding of large data. Classification predicts categorical and a prediction model predicts continuous valued functions. For example, we can build a classification model to categorize bank loan applications as either safe or risky, or a prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation….

Clustering in Data mining

clustering is a technique to prepare the group of similar data objects based on their internal similarity. the data objects are representing the properties or features of an individual pattern of data. these properties or features are used for computing the similarity or differences among the data objects. the data objects which are grouped is termed as the cluster of data. the cluster analysis includes two main components first is known as centroid and second is data objects or cluster members. Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. This clustering analysis allows an object not to be part of a cluster, or strictly belong to it, calling this type of grouping hard partitioning [1]. In the other hand, soft partitioning states that every object belongs to a cluster in a determined degree. More specific divisions can be possible to create like objects belonging to multiple clusters, to force an object to participate in only one cluster or even construct hierarchical trees on group relationships There are…

Mobile Ad-Hoc Networks
Networking , Technology & Science / August 10, 2017

MANET Overview The new generation network that includes the wireless devices which are able to move freely or independently in random directions and speed with regulation of any authority and connected through the wireless links can be termed as ad-hoc networks. Fast expansion of wireless communication technology and the broad usage of mobile communication tools, wireless ad hoc networks are getting more and more consideration. Nowadays, wireless ad hoc networks are not only used in military, but also been applied to civilian application, including home area networks, mobile communication networks, and so on. With the widespread rapid development of computers and the wireless communication, the mobile computing has already become the field of computer communications in high-profile link. An Ad hoc network is a collection of mobile nodes, which forms a temporary network without the aid of centralized administration or standard support devices regularly available as conventional networks. These nodes generally have a limited transmission range and, so, each node seeks the assistance of its neighboring nodes in forwarding packets and hence the nodes in an Ad hoc network can act as both routers and hosts. Thus a node may forward packets between other nodes as well as run user applications….

What is web mining

Overview of web mining Internet is a large source of data and information; the data on web is frequently accessed and changed. Important and knowledgeable information extraction form the World Wide Web is the application of data mining techniques. Figure 1 categories of web mining The technique of exploring the web data using the data mining algorithms is termed as web mining in order to recover the significant patterns over the data. The information in web can be available directly by using contents and links or indirectly by using the access logs or other kinds of log formats. According to the application of mining algorithms or techniques the web mining can be categorized in three main classes: Web content mining: this technique is also known as text mining, generally the second step in Web data mining. Content mining is the scanning and mining of text, pictures and graphs of a Web page to determine the significance of the content. Web structure mining: that is one of three categories of web mining, it is a tool used to recognize the connection between web pages linked by information or direct link connection. This organization of data is discover-able by the condition of web structure…

What is text mining

When the data mining techniques and algorithms are utilized with the unstructured source of digital documents such as text file, web documents or others. that process is known as text mining. In web mining the content mining includes the techniques of text mining for finding text based patterns from web documents. Basically text mining approaches, required much effort to find specific pattern from data. First text mining approach is introduced in mid-1980s. But technological progress have allow to improve previous issues continuously. Text mining is a domain that having a wide range of applications in information retrieval, machine learning, data mining, statistics, and computational semantics. According to the different definitions of text mining it can be: “This kind of system able to gain information across languages and also capable to group similar data from different kind of language sources according to their original semantics.” A Business Intelligence System where text mining for unstructured data is addresses as the major issue, which describes a system that will: “Utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the ‘action points’ in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a…

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