Mining Frequent Pattern Using FP Tree Algorithm for Optimised Time Complexity In Different Datasets

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.

A scalable decision tree system and its application in pattern recognition and intrusion detection

Analytic tools are useful for detecting patterns in education data and providing insights about student performance and learning. This study compared six supervised learning algorithms (linear regression, ridge regression, the lasso, regression trees, random forests regression, gradient boosted regression) and identified features important for predicting student performance. The dataset consisted of N=1044 observations from two secondary schools in Portugal (UCI-MLR, Cortez & Silva, 2008). Performance was assessed by final grades (range: 0-20) in two courses, mathematics and Portugese. The models were fit to training data with 27 independent variables and evaluated on a testing subset. Overall, performance was lower for students in mathematics than Portugese. The models selected a similar set of variables as important for predicting performance: mother's education level, student plans for higher education, and weekly study time were positively related to predicted performance, whereas course subject, school educational support, and romantic relationships were associated with decreased student performance. The models differed in the number, weighting, order and importance given to predictor variables. Linear regression provided a model with 13 predictors. Ridge regression shrank the coefficient estimates toward zero; the lasso performed variables selection for a model with 20 predictors. There was a tradeoff between model complexity and interpretability. The single pruned regression tree provided a simple, interpretable non-linear model with four features. Random forests regression and gradient boosting reduced overfitting, but were more difficult to interpret. Advantages and limitations of the different models are discussed. Applications for educational data mining (EDM) and learning analytics (LA) are considered.

We address the multicast issue in a wide area WDM network in which a source broadcasts its message to some sites in the network. This problem can be formalized as the following optimal multicast tree problem. Given a directed graph G = (V, E) with a source s and a set S of nodes, assume that every node in S (⊂ V) is reachable from s. Associated with every link e ∈ E, there is a set Λ(e) of available wavelengths on it, |V| = n and |E| = m, find a multicast tree rooted at s including all the nodes in S with minimizing the cost of the tree, in terms of the sum of the cost of wavelength conversion at nodes and the cost of using wavelengths on links in the tree. That is, not only do we need to find a directed tree rooted at s, but also do we need to assign a specific wavelength Λ ∈ Λ(e) to every tree edge e and to set the switches at every node in the tree. Since this problem is NP-complete, it is unlikely that there is a polynomial algorithm to find an exact solution for it. Instead, we first present an approximation algorithm for the problem which runs in time O(k2n + km +kn log(kn) + (kn)1/e|S|), and delivers a solution within O(|S|e) of the optimal, for any fixed 0 > e ≤ 1. We then present a distributed version of the proposed algorithm. The communication and time complexities of the distributed algorithm are O(km) and O(kn) respectively, and the solution delivered is |S| times of the optimal, where k is the number of wavelengths in the network. Finally, we conduct some experiments to verify the applicability of the proposed algorithms. The experimental results matches our algorithmic claims.

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

This paper presents a new efficient algorithm for mining frequent closed itemsets. It enumerates the closed set of frequent itemsets by using a novel compound frequent itemset tree that facilitates fast growth and efficient pruning of search space. It also employs a hybrid approach that adapts search strategies, representations of projected transaction subsets, and projecting methods to the characteristics of the dataset. Efficient local pruning, global subsumption checking, and fast hashing methods are detailed in this paper. The principle that balances the overhead of search space growth and pruning is also discussed. Extensive experimental evaluations on real world and artificial datasets showed that our algorithm outperforms CHARM by a factor of five and is one to three orders of magnitude more efficient than CLOSET and MAFIA.

The current and future business processes would be service oriented. The required service is selected using discovery process. The discovery process uses in-memory parse tree construction or callback based document parsing. Existing service discovery systems stores both functional attributed and QoS attributed in tree data structures and they are time consuming, space consuming, requires frequent parse tree construction in various location, difficult to replicate and distribute parse tree and replicated parse trees are inconsistent. This paper proposes an efficient methodology to improve the performance of in memory parse tree discovery process over extended web service architecture. The system uses two parallel discovery algorithms, Synchronous and Asynchronous algorithms for service discovery, both algorithms uses the tree and indexed data structure for service discovery and the later also uses a bitmap table to verify functional requirements. The services are selected and ranked based on user request using Coefficient of Variance. The proposed algorithms are detailed in this paper. The experimental results are conducted on benchmark dataset are discussed. The proposed system ensures availability, consistency, effective space utilization, eases replication and distribution and improves response time for service discovery by 30%.

Mining people's semantic trajectory behaviours from geotagged photographs

The degree constrained minimum spanning tree problem is to determine a spanning tree of the minimum total edge cost and degree no more than a given value d (d-MST). A number of algorithms have been proposed for this problem. In [1] we introduced a new spanning tree called vertex subset degree preserving spanning tree, which was defined as a spanning tree T such that , v A-a non empty subset of the vertex set V of the graph G. This paper presents two algorithms to generate all degree constrained spanning trees and all vertex subset degree preserving spanning trees of a weighted graph in order of increasing cost. By generating spanning trees in order of increasing cost, it is possible to determine the second smallest or in general the k-th smallest spanning tree of a graph. Time complexity analyses are also given.

The grid-based clustering algorithm is an efficient clustering algorithm, but its effect is seriously influenced by the size of the predefined grids and the threshold of the significant cells. The data space will be partitioned into a finite number of cells to form a grid system and then performs all clustering operations on this obtained grid system. A critical problem needed to be noticed here, no one knows the distribution of the clustering data in each dimension. So, a method to reduce the influence of the size of predefined cells and the density threshold of significant cells is a must. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells and inherits the advantage with the low time complexity, we propose “A Crossover-Imaged Clustering Algorithm with Bottom-up Tree Architecture” in this dissertation. The data space is separating into cells which are organized the first grid system, and the data space is transformed into the new grid system. The chosen density threshold and some predefined cells are utilized to identify the significant cells and semi-significant cells. With our clustering algorithms, the size of significant cell and its density threshold are easy to select to group the significant cells into ideal clusters. Next, the data cells are shifted to define the new grid system. Then the semi-significant cells are selected to group the significant cells and other semi-significant cells to be clusters. Then, we build the bottom-up tree architecture with four levels, root (set of clusters), combination (semi-significant cells), connection (pre-clusters), leaf nodes (significant cells), and define the semi-significant cells to group the clusters. Finally we will verify by experiment that the results of our proposed algorithms can reduce the influence of the size of predefined cells and the density threshold of significant cells. And the proposed algorithms still inherit the advantage with the low time complexity and require at most one single scan through the data and one cell clustering.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

The identification of useful patterns from a medical dataset benefits early diagnosis. Classification techniques in data mining are famous for medical data prediction. Decision Tree (J48) and Navie Bayes are the most popular and frequently used algorithms in the field of prediction analysis. The efficiency of such prediction algorithms can be improved with better pre-processing approaches. This paper aims at finding optimum features using permutation combination of input data attributes for improving the classifier accuracy. The Pima Indian Diabetes Dataset from UCI repository is used for experimentation. The performance of J48 and Navie Bayes has been tested for different combination of features. Keywords: Diabetes; J48; Navie Bayes; Permutation

Diabetes is the common chronic disease and a major health challenge in all population. Gestational diabetes mellitus (GDM) is a type of diabetes developed in women at the time of pregnancy. We present a Data mining (DM) approach to identify the risk factors of Gestational diabetes mellitus (GDM) using different data mining techniques. Dataset used for analysis contains the details of the pregnant women admitted the local hospital of Mysuru, India. The data mining techniques used are k-means clustering, J48 Decision Tree, Random-Forest and Naive-Bayes classifier. Classification accuracy is enhanced by using feature subset selection wrapper approach. Data imbalanced problem is handled by using Synthetic Minority Over-sampling Technique (SMOTE). The performances of the algorithms have been measured and compared in terms of Accuracy.

Ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines (SVMs) have also been proposed for tackling it, but the scalability aspect of these approaches to handle large datasets still needs much of exploration. In this paper, we will extend the recent proposed algorithm Core Vector Machine (CVM) to the ordinal-class data, and propose a new algorithm named as Ordinal-Class Core Vector Machine (OCVM). Similar with CVM, its asymptotic time complexity is linear with the number of training samples, while the space complexity is independent with the number of training samples. We also give some analysis for OCVM, which mainly includes two parts, the first one shows that OCVM can guarantee that the biases are unique and properly ordered under some situation; the second one illustrates the approximate convergence of the solution from the viewpoints of objective function and KKT conditions. Experiments on several synthetic and real world datasets demonstrate that OCVM scales well with the size of the dataset and can achieve comparable generalization performance with existing SVM implementations.