The Growth Of Gps Technology Essay

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

Data mining enables users to discover hidden patterns without a predetermined idea or hypothesis about what the pattern may be. The data mining process can be divided into two categories: discovering patterns and associations, and predicting future trends and behaviors using the patterns. The power of data mining is evident as it can bring forward patterns that are not even considered by the user to search for. Hence, you have the answer to a question that was never asked. This is especially helpful when you are dealing with a large database where there may be an infinite number of patterns to identify. Interesting to note is the fact that “the more data in the warehouse, the more patterns there are, and the more data we analyze the fewer patterns we find.” What this means is that when there is richness of data and data patterns, it may be best to data mine different data segments separately, so that the influence of one pattern does not dilute the effect of another pattern in a large database.

With the advent of machine learning and its potential in getting best out of any application, even the data mining played the game of harnessing the power of machine learning. Needless to say, SVM is one of the very powerful and revolutionary algorithms in the field of machine learning due to its efficiency in classifying. In this report, my concentration mostly lies in discussing the applications of SVM in Data mining and analyzing the performance. Data mining is very important and essential technique in the field of analytics. The principle being extracting use full information from a massive data source and using it as an input for improvement or development. When we have a huge amount of data and equally less amount of information, data mining is one technique that enables to get better information out of the data. However, it 's not very easy to do the analysis part on huge datasets, and hence machine intelligence is introduced into the field of data mining.

Association rule mining, One of the most important approaches and techniques that studied of data mining. A lot of researchers’ Inefficient the ideas to get the frequent item sets/ pattern sets. frequent patterns and closed frequent patterns are two techniques the major objective it is to reducing the set of extracted patterns to a smaller more interesting subset. Some frequent pattern mining often generates a large number of frequent patterns, which imposes a big challenge on visualize, comprehension and further analysis of the generated patterns. Mine a large amount useless frequent patterns , it is requires more space and more time, and thus leads to high cost as well as ineffectiveness. This require the need for finding small number

Based on these trends, large amount of data are being gathered and stored in databases, and data warehouses. The huge volume and fast pace made the power of data much stronger than what we expected, with lots of potential waiting us to maintain, explore and make decisions about. Using the efficient way to analyze the most helpful and valuable data, as well as to find out the hidden data is becoming urgent and important. Because of these needs, data mining started to be used as a helpful technology, and plays an important role under today’s studying and working environment.

Therefore, to randomly access these datasets, which is commonly assumed in traditional data mining, is really expensive.

Trajectory clustering with probabilistic modeling of a set of trajectories is proposed in paper\cite{gaffney2007probabilistic}. Whole trajectories may miss interesting common paths in sub-trajectories. Lee et al. \cite{lee2007trajectory} proposed a partition-and-group framework that finds out an interesting common path in sub-trajectories. Mao et al. \cite{mao2016mining} proposed mining spatiotemporal patterns of urban dwellers from taxi trajectory data. The method involves three critical steps that are discover potentially meaningful locations using spatial clustering of taxi origin-destination, identifying perfect parameter values to extract jobs housing urban infrastructure and visualization of this spatial distribution and temporal trends of the revealed urban infrastructure. Another swarms mining method on big spatiotemporal trajectories using MapReduce \cite{yu2016mr} has been proposed to mine swarm pattern. They propose a parallel model based on timeset independent property where data sets are divided based on time and creates local swarm patterns. Even though, combining these local swarms, global swarms have been created. Our observation is that partitioning datasets based on time lost data consistence and significant number of patterns removed from local swarms and in addition in huge number of datasets, map and reduce process takes extra time for mapping and reducing. Chen et al. \cite{chen2017mining}

Big Data is a useful tool that helps in collecting, analyzing and disseminating any form of information that may start from simple phones and ranging up to enormous super computers to analyze the movement of pattern, behavior of people towards data and patterns that are developed out of data analysis (LaValle, 2013). Data mining techniques are also aided by the concepts of big data to scoop out some of the useful information in various fields and researches of study. The pattern analysis can be aided with the help of text messages via cell phones. It is almost next to impossible to trap the data generated by millions of users but with the invention of this new technique and plethora of tools aiding the process of data analysis, the cell phone data generated by approximately six billion of

Data mining is a new technology which could be used in extracting valuable information from data warehouses and databases of companies and governments. It involves the extraction of hidden information from some raw data. It helps in detecting inconsistency in data and predicting future patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm and framework, and the automated analysis provided by this algorithm and framework go ahead of evaluation in dataset to providing solid evidences that human experts would not have been able to detect due to the fact that they

Data mining prediction model works on the process of identifying the patterns based on the historical information to predict the new incoming data sets. This prediction modelling is much useful in the case of decision making process in the business models. On the other way, Descriptive model describes the data in an efficient way by means of grouping the data by using clustering; association rules principles of data mining.

Data mining is the process of extracting knowledge from large data sets. It uses artificial intelligence methods to discover the hidden relationships among the huge amount of data that is collected. It has a great potential to improve applications in many fields like Healthcare systems, Customer relationship management, Financial banking, Research analysis, Bio informatics, Marketing analysis, Education, Manufacturing engineering, Criminology and many more. Criminology is the study of crimes and typically a criminologist’s job include analyzing data to determine why the crime was committed and more importantly to predict and prevent criminal behavior in the future. It became an interesting field to apply data mining techniques because of its large datasets and the complexity of relationships between the data. This paper will discuss some of the tools and techniques used in this field to find out important information that will help and support the police forces and reduce social nuisance.

Data mining is the process of extracting useful knowledge from large databases or data warehouses. It can be also said as a set of mathematical functions and data manipulation techniques to extract useful data from databases. Data mining can also be said as knowledge discovery process in other words. It explores a large collection of data into a meaningful patterns and rules based on the queries provided by users using data mining query language. The meaningful patterns and rules are generated by analysing the database. Data mining makes use several techniques such as clustering, classification, association rule mining and so on to generate the meaningful patterns from the databases. The purpose of this report is to describe how data are prepared for data

Abstract— Data mining is the method of extracting the data from large database. Various data mining techniques are clustering, classification, association analysis, regression, summarization, time series analysis and sequence analysis, etc. Clustering is one of the important tasks in mining and is said to be unsupervised classification. Clustering is the techniques which is used to group similar objects or processes. In this work four clustering algorithms (K-Means, Farthest first, EM, Hierarchal) have been analyzed to cluster the data and to find the outliers based on the number of clusters. Here the WEKA (Waikato Environment for Knowledge Analysis) for analyzing the clustering techniques. Here the time, Clustered and un-clustered

Computer-based technologies have lot of impact on the way we live, work, and play and socialize. The methods of analytics and datamining had changed over the past few decades. In 1970s datamining played an important role in scientific fields such as physics, biology, and climate science. In mid-1990s and into the first decade of the new millennium, data mining and analytics played important role in business practices.

Modern hardware is advance to a level where it can collect huge data with a high rate. These make storing all the data nearly impossible. This gave rise to idea to process the data online to perform several queries. This paper discusses various data stream mining technique, current state of the art in streaming algorithms and the challenges.