1. Introduction
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). Cluster analysis is an unsupervised form of learning, which means, that it doesn't use class labels. This is different from methods like discriminant analysis which use class labels and come under the category of supervised learning. K-means is the most simple and popular algorithm in clustering and was published in 1955, 50 years ago.
The advancement in technology has led to many high-volume, high-dimensional data sets. These huge data sets provide opportunity for automatic data analysis, classification
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Learning means that given some training data set, we want to predict the class labels of the testing data set. Apart from supervised learning in which class labels are known and unsupervised learning in which class labels are unknown, there is a third type of hybrid learning called semi-supervised. In this type of learning, we have class labels for some portion of the training set. But instead of discarding the large portion of training set with unlabelled data, it is also used in the learning process. Instead of using class labels, pair-wise constraints are used. According to the must-link constraint two objects should be assigned to the same cluster while cannot-link constraint specifies that the cluster labels of two objects should be different.
2. Data Clustering
"The goal of cluster analysis is to discover natural grouping of a set of patterns, points or objects."
Clustering can be defined on the basis of similarity, such that the intraclass variation is low while the interclass variation is high. Clusters differ in terms of shape, size and density. If there is noise in the data, then detection of cluster becomes even more difficult. "An ideal cluster can be defined as a set of points that is compact and isolated." In reality, the interpretation of cluster requires domain knowledge. Even though humans can seek clusters in two and three dimensions, algorithms are required for high dimensional data. In addition to this, the number
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Examples: images, text, audio, video etc. They don't follow any specific format. Structured data on the other hand has semantic relationships within objects. Most clustering approaches use a vector based feature representation, instead of the structures in the object.
Clustering ensembles
This method earlier used for supervised learning, is now also done for unsupervised learning. Multiple partitions called clustering ensembles are obtained by taking multiple looks at the same data. These multiple partitions are combined together and give a good partitioning result, even if the individual clusters were not good enough. Multiple partitions can be generated in various ways. Applying different clustering algorithms, applying same algorithm with different parameters or combining different feature representations and clustering algorithms are some of them.
Semi supervised learning
Any extra information along with the n x d pattern matrix or n x n similarity matrix helps in determining a good cluster. The algorithm using the extra information is said to be operating in a semi supervised mode of learning. This side information can be specified in forms of constraints like must-link and cannot-link, or seeding, where small amount of labelled data is given along with large unlabelled
With widespread use of internet services, the network scale is expanding on daily basis and as the network scale increases so will the scale of security threats which can be applied to system connected to the network. Viruses and Intrusions are amongst most common threats that affects computer systems. Virus attacks can be controlled by proper antivirus installation and by keeping the antivirus up to date. Whereas any unauthorized access in the computer system by an intruder can be termed as Intrusion and controlled by IDS. Intruders can be grouped into two major categories which are external and internal Intruders.
The book Freakonomics by Steven D. Levitt and Stephen J. Dubner talks about many different things, including cheating teachers and sumo wrestlers, how abortion lowered crime rates, how a street crack gang works, and whether the way parents raise their children even matter. These topics seem to have nothing in common, but all of these topics were identified in the same way: an economist (Levitt) looked at school test scores, crime data, and all sorts of other information, looking at them in unconventional ways. Because of that, he has come to many interesting and unique conclusions that make complete sense. These findings were based on some simple ideas: the power of incentives, conventional wisdom is not always right, things may not have obvious causes, and experts often serve their own interests instead of the interests of others. Perhaps the most important idea in the book is, as Levitt and Dubner state, “Knowing what to measure and how to measure it makes a complicated world much less so” (14).
Answer: (a): Market segmentation is the first step in defining and selecting a target market to pursue and penetrate. Basically, market segmentation is the process of splitting up an overall market into two or more groups/classes of consumers. Each group of consumers is called as a market segment. Each group (or market segment) should be similar in terms of certain characteristics or product/ service needs. In business world, market segmentation is considered to be a most important tool in enabling marketers to better meet customer needs and requirements.
Even though organizations hold huge amount of data, they cannot use them effectively as they are unstructured. However new technologies are now available which enable analysis of large, complex, unstructured data. The accessibility of technology has become easy; as a result, there is massive increase in data amounts available with the entrepreneurs. The data usage depends on the ability the way it is stored, managed and then analyzing it adequately. Big data is an upcoming and emerging trend in the field of Information technology.
The firm procures raw materials and components across the world and continually examines its production requirement against its manufacturing capacities to pursue cost reduction. Capabilities Thanks to the cloud, Revlon has been able to resolve the difficulties of big-data management efficiently by classifying all the unstructured data in the company (Swan,
In this essay, I will discuss the key premises of symbolic interaction as well as consider the ways in which symbolic interaction promotes the view that people have agency. I will then put forth the argument that conflict theorists make with respect to schools reproducing the culture of the dominant class. In relation, I will mention in what ways this perspective promotes the view that people are constrained by social structure. Finally, I will discuss the dialectical relationship between structure and agency "Symbolic interactionism has come into use as a label for a relatively distinctive approach to the study of human group life and human conduct."
Learning describes the changes in an individual’s behaviour arising from experience and verifies that most human behaviour is learned. For example, when you go to somewhere, different countries have different background and also have different products. If the community or one’s friends has a majority number of iPhone users, they will eventually introduce and give us the information about an iPhone or Apple products that they use. Thus these are one of the factors that trigger Apple users to buy Apple products.
These concept is also known as STP (Segmentation, Targeting
Tasting Success Article Page 95 Discussion Questions Question 1 Which decisions in this story could be considered unstructured problems? And structured problems? Structured problem Can be defined as a straightforward, familiar and easily defined issue, and it is easily solved by the eight step-by-step process Identify a Problem, Identify Decision Criteria, Allocate Weights to the Criteria, Develop Alternatives, Analyze Alternatives, Select an Alternative, Implement the Alternative and Evaluating Decision Effectiveness. The issue as described in the article is the orange juice production and it is considered as a structured problem, and the way it is produced, its mechanism is responsible for the production as it is based on Coca-Cola’s mixture
Big Data There are many different definitions for Big Data. SAS (n.d.) an analytical software company describes it as, “a popular term used to describe the exponential growth and availability of data, both structured and unstructured.” Many think Big Data just came into existence but it has been around for years. Banks, retail, advertisers have been using big data for marketing purposes.
According to TrackMaven, market segmentation is the process of dividing the market of potential customers into groups, or segments, based on different features. The created segment consists of consumers who will respond to the same marketing strategy and who share the nature of the same interests, needs, or locations. McDonald uses demographic segmentation as their main types of market segmentation. According to Sakshi Natani (2016), McDonald in Malaysia used mainly demographic segmentation, which divided in age, income, family-life cycle and social class.
The process of market segmentation involves the division of a market into groups of smaller size whose needs, behaviour and characteristics are distinct from each other. These smaller groups or 'segments ' may require separate marketing strategies. There are four major market segmentation variables namely behavioural, psychographic, geographic and
Alphanumeric data, Numbers, Characters, Image data, Graphic shapes are the different forms of data. It also includes audio and
1. Student details: 1.1 Name: Vaghela Deepikaben Maganbhai 1.2 Student ID:1525258 2. The programme of research 2.1 Title: To evaluate customer satisfaction in restaurant industry in India. 2.2 Research Objectives: • To explore the relationship exist among these factors, employee performance, food quality, price, physical environment and customer satisfaction with the help of literature review.
1. MARKAT SEGMENTATION Market segmentation is a strategy that is generally used by a company to identify and define the target customers, and provide the supporting data for the marketing plan elements. There are five types of market segmentation which are demographic segmentation, geographic segmentation, psychographic segmentation, benefits segmentation and volume segmentation. • Demographic Segmentation Demographic segmentation is market segmentation according to age, family size, religion, race, gender, income and education. By using this segmentation, a company can categorize the needs of consumers more easily and target its consumers more accurately because demographics can segmented into several markets.