Clustering Methods

The clustering hierarchy for text clustering or documents clustering we used cluster dendrogram presentation by R implementation. Some people find cluster dendrograms to be fairly clear to read. Others simply find them perplexing. Here, we can see many more groups that are identifiable in the dendrogram. Here, I have arbitrarily chosen to look at five clusters, as indicated by the red boxes in figure

Through initial HCA application we identified and eliminated from our analysis two Sydney suburbs revealed through the analysis as unique ‘outliers’ (in socio-economic change over time, and ‘overseas migration churn’ respectively). Having eliminated the associated distortion, a k-means cluster analysis was undertaken for the remaining 175 disadvantaged areas, based on the four area groupings which emerged from our initial HCA results. This produced a more balanced assemblage of members within each category – see Table 2. As shown here, area Types 2 and 4 both encompassed the majority of disadvantaged suburbs (77%), and also contained most of the relevant population within them (89%). As suggested by

The analytical methods applied on security pattern data yield results in the form of pattern clusters and concepts. Pattern applicability can be improved by making this classification and resulting concept knowledge readily available to practitioners. In order to make it readily available, the results must be represented in a suitable format. Further, it must be shared using a tool or technology which is easy to use and does not need any special hardware or software. In this chapter, we present our approach towards representing the gained knowledge and sharing it so that it can be accessed and used. The chapter outline is as follows. In Section 7.1, we present the knowledge discovery and its adoption for the present work. The first mining tool applied is clustering. The knowledge obtained using this mechanism is presented in Section 7.2. The second knowledge discovery technique used in the research is FCA. Section 7.3 gives an overview of the knowledge extracted using FCA. In Section 7.4, we give our views about knowledge sharing, the issues which need to be considered and our solutions to address them. The representation format for various knowledge elements is presented in Section 7.5. The knowledge search framework is presented in Section 7.6 followed by a discussion on the implementation aspects in Section 7.7. The chapter ends with a summary.

Data is often far from perfect. Most of the data mining technique can tolerate and bare some level of imperfection and inconsistency in the data. This imperfection and inconsistency may cause loss of individual’s private data. Preserving privacy data mining focuses on obtaining positive result analysis. The data quality issues that often need to be addressed includes the presence of noise and outliers, missing inconsistent or duplicate data, biased and unrepresentative phenomenon and that the data is supposed to be. For preprocessing and pattern recognition it is mandatory to build the data in representable form before providing it to data mining techniques. In this paper we introduce Noise cure framework as a bridge between data preprocessing and data mining for preprocessing the raw data to bring in a form suitable for pattern recognition in preserving privacy data mining

This splitup function helps in coming out of the problem associated with DBSCAN as it aims to partition highly uniform datasets, which may be incorrectly identified as a single cluster if DBSCAN is called with a reasonable large s-reachability value (Usually the density based algorithm forms relatively less clusters based on the density even when there is a need to further divide the clusters). This splitup function is also useful for some datasets that present specific challenges for the density based algorithm such as those with large discrepancies in density. When there is a situation which leads into a dilemma in which clusters to split, this function takes the strength of the cluster as well as the distance between the new clusters

K-means clustering works in this capacity by portioning n number of observations into k number of clusters. Each n is associated with a k based on the mean. The analysis is complete when each observation is assigned a cluster that is closest to its arithmetic mean (Nekola, 2004). K-means clustering results in several clusters which can then be assigned to tree, shrub, understory or other related layers. The technology used to create such DEM’s began with early laser survey techniques.

Abstract: Data clustering is the most important technique in studying data analysis and it is also important in researching several domains regarding the analysis for which sampling has been important to improve the efficiency of clustering. However, after the sampling applied those points that are not selected in sample have their labels after the normal process and even we have so many straight forward approach in numeric domain we have the problem of allocated these unlabeled

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}

Cluster analysis has many different algorithms and methods to classify objects(Saunders, 1994). One of the challenges faced by the researchers in different areas is to organize their data which is possible by cluster analysis, it is a data analysis tool which focus on classifying the different objects into groups such that the degree of association of the objects in a same group is highest if they belong and least if they do not belong. Cluster analysis is a simple term, it does not identify any statistical method or model and also there is no need to make any assumptions about distribution of data, it is used to form groups of relevant variables without providing any explanation (Stockburger, n.d.).

C. Ambroise et al., choosing a clustering algorithm that is well-suited for dealing with spatial data. In this algorithm, derivative from the EM algorithm has been designed for penalized likelihood estimation in situations with unobserved class labels and very satisfactory empirical results lead us to believe that this algorithm converges [3].

Data mining is the process of extracting hidden information from the large data set. Data mining techniques makes easier to predict hidden patterns from the data. The most popular data mining techniques are classification, clustering, regression, association rules, time series analysis and summarization. Classification is a data mining task, examines the features of a newly presented object and assigning it to one of a predefined set of classes. In this research work data mining classification techniques are applied to disaster data set which helps to categorize the disaster data based on the type of disaster occurred in worldwide for past 10 decade. The experimental comparison has been conducted among Bayes classification algorithms (BayesNet and NaiveBayes) and Rules Classification algorithms (DecisionTable and JRip). The efficiency of these algorithms is measured by using the performance factors; classification accuracy, error rate and execution time. This work is carried out in the WEKA data mining tool. From the experimental result, it is observed that Rules classification algorithm, JRip has produced good classification accuracy compared to Bayes classification algorithms. By comparing the execution time the NaiveBayes classification algorithm required minimum time.

For measuring the quality of clusters four criteria have been used. The first three criteria are designed so as to measure the quality of cluster sets at different levels of granularity. Ideally it’s needed to generate partitions that have compact, well separated clusters. Hence, the criteria used presently combine the two measures to return a value that indicates the quality of the partition thus the value returned is minimized when the partition is judged to consist of compact well separated clusters with different criteria judging different partition as the best one. The last criterion is based on time efficiency.

Abstract— This All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.

I will conduct a comparative analysis of multiple force-directed algorithms used to identify clusters in biological networks. The analysis will consider topics such as the algorithm process, amount of preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K-Means, SPICi, Markov Clustering, RNSC, and PBD will be used for the comparison. I will identify the best algorithm according to my analysis for each type of input data studied.

Abstract-This paper gives a brief description of the above titled paper. Data clustering is one of the most widely used method for various applications. And parallelizing these time-consuming applications is of quite importance. This paper brings out an additional feature of handling input data of various dimensions and thus accordingly handle it.