Advantages And Disadvantages Of Birch

Boreal forests are widespread of vegetation and trees that are prone to starting forest fires. Due to this factor, the survival of boreal species, specifically those in North America, will vary.

‘The toadstool wood’ describes a woodland at twilight. Everything is quiet and dark. Reeves describes the woods as if it is hoary and inactive, I know this because in the poem he says ‘mouldy’. He also says ‘arching sprays of bramble’ which means there would be alot of cultivation as well in the wood. On the other the poem, ‘stopping by Woods on a snowy evening’ has the setting of a man on horse back stopping by the woods for a rest when he is on a long journey. Frost describes these woods as if it is glacial and gloomy, I know this because in the poem he says ‘The darkest evening of the year’. This would also means that it is frightening.

For centuries wood has been utilised by many great civilisations. With its natural form, it is vulnerable to decay and degradation over time. Wood preservation has become more and more important over the years because of the efforts to grow timber more rapidly and convert it more economically have likely reduced the durability of the end product (Ridout, 2000).

Both data mining and data analysis are a subset of Business Intelligence which also includes data management systems, data warehouses and Online analytic processing(OLAP). To manage the mountains of information, the data is put away in a warehouse of information accumulated from different sources, including corporate databases, compressed data from interior frameworks, and information from outer

Nowadays, data mining and machine learning become rapidly growing topics in both industry and academic areas. Companies, government laborites and top universities are all contributing in knowledge discovery of pattern recognition, text categorization, data clustering, classification prediction and more. In general, data mining is the technique used to analyze data from multi perspectives and reveal the hidden gem behind the enormous amount of data. With the explosive growth of data collections, it becomes time-consuming less effective to extract valuable information from massive databases through the use of traditional data analysis methods. An alternative way to solve this problem is to apply data mining, given considerations

Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyse this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation [12].

Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009).

This report will presents the various features of data mining tools and emphasize the importance of data mining to realize the value of data warehouse.

For any given point in a high dimensional data space, the expected gap between Euclidean distance to the closest neighbor and to the farthest point in the space is minimized as the dimensionality grows [5]. Therefore, the notion of nearest neighbor is meaningless. This may lead many data mining tasks, like clustering, to become largely ineffective, as the model becomes more and more susceptible to noise present within the data.

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

Chuang and Chien [2004] proposed to cluster and organize users’ queries into a hierarchical structure of topic classes. A Hierarchical Agglomerative Clustering (HAC) [25] algorithm is first employed to construct a binary-tree cluster hierarchy. The binary-tree hierarchy is then partitioned in order to create subhierarchies forming a multiway-tree cluster hierarchy like the hierarchical organization of Yahoo [6] and DMOZ [3].

As one of the most popular and widely used data mining techniques, cluster analysis is mainly divided into hierarchical clustering and partitional clustering, which are carried out in a supervised or unsupervised way to separate data into different groups based on similar characteristics. Both the hierarchical and partitional clustering have advantages and drawbacks. Especially, the efficiency and accuracy are the primary challenges that clustering analysis has to face. For example, the most efficient algorithm of hierarchical clustering is complete-linkage clustering in some special cases, the complexity of which is Ο (n2). Therefore, the hierarchical clustering usually leads to too slow efficiency for large data

Data stream mining refers to the extraction of informational structure from continuous and rapid data streams. It poses a lot of challenges varying to different aspects that include problems related to storage, computational, querying and mining. For the past recent years, data mining research have gained due to an extra ordinary increase in computing power and increasing data of streaming information. Due to an increase number in the database sizes and computational power, many algorithms and techniques have been developed within the past few years. Because of these data stream requirements, it is important to design new techniques other than the traditional methods that would require the data to be first stored in their database and then pre-processing it using scientific and complex algorithms that makes several pass over data and the data is generated with high rate which is very difficult to store. This causes two research challenge:

Abstract - This paper presents the analysis of Kmeans and K-Medians clustering algorithm in detecting outliers. Clustering is generally used in pattern recognition where if a user wants to search for some particular pattern, clustering reduces the searching load. The k-means clustering and kmedians clustering algorithm’s performance in detecting outliers are analysed here. K-means clustering clusters the similar data with the help of the mean value and squared error criterion. Kmedians is similar to k-means algorithm but median values are calculated there. Outliers are the one different from norm. If they are not properly detected and handled, they clustering will be affected in a great manner.

In our future work, we will be calculating the values of support and confidence on the multimode cluster using Apriori pruning. In this algorithm every item is considered as a 1-itemset. The support for each item is calculated and compared against the input support value entered by the user. If the support values of the item sets are less than the input support then those candidates are discarded and the action rules discovery algorithm only runs on the remaining attributes. This process is continued for 2–item sets and 3 and so until we have the support values for the item sets less than the input value. For example if we get the input support value to be 5 and the 4 item set attributes lead to support value less than 5, then we will be considering only the attributes that form the 4 item sets. In this method, we are pruning the attributes before the action rules are discovered and the algorithm only run on the attributes that form the frequent item sets. This avoids unnecessary combination of rules within the attributes. Instead of generating the rules at a later stage after finding all the combinations of rules, this prunes early to avoid unnecessary combinations. This greatly increases the time complexity of the algorithm and as well as space complexity. We are also planning to run this algorithm in the MapReduce Framework similar to the one we have done in this paper. This further decreases the time complexity and the action rules discovery