Quality Analysis of Telemetry Tracking and Command at Ground Stations using the Association Rule Mining Approach

LAPAN built several remote ground stations to support the telemetry tracking and command (TTC) system for the LAPAN-A2 and LAPAN-A3 satellites. These remote ground stations are located in Kototabang/KT (West Sumatra), Biak/BK (Papua), Parepare/PR (South Sulawesi), Rumpin/RP, Rancabungur/RB (Bogor, West Java), and Svalbard/SV (Norway). Problems that often arise in the TTC process are telecommands not being sent (commands sent from the ground station to the satellite) or telemetry packages not being received (feedback on telecommands sent by the satellite to the ground station). This research attempted to calculate and analyze the quality of TTC using a data-mining approach, i.e., rule mining. The calculations were performed using five main parameters: satellite name, ground station, azimuth, altitude, and communication status. The research output consisted of a combination of remote ground station parameters that may result in a successful or failed TTC. For the LAPAN-A3 satellite at the Svalbard ground station, 19 failed communication combinations were generated with a dataset of 57,029. Communication failures occur in azimuth and elevation, i.e., areas blocked by obstacles

Finding a short and accurate decision rule in disjunctive normal form by exhaustive search

Greedy approaches suffer from a restricted search space which could lead to suboptimal classifiers in terms of performance and classifier size. This study discusses exhaustive search as an alternative to greedy search for learning short and accurate decision rules. The Exhaustive Procedure for LOgic-Rule Extraction (EXPLORE) algorithm is presented, to induce decision rules in disjunctive normal form (DNF) in a systematic and efficient manner. We propose a method based on subsumption to reduce the number of values considered for instantiation in the literals, by taking into account the relational operator without loss of performance. Furthermore, we describe a branch-and-bound approach that makes optimal use of user-defined performance constraints. To improve the generalizability we use a validation set to determine the optimal length of the DNF rule. The performance and size of the DNF rules induced by EXPLORE are compared to those of eight well-known rule learners. Our results show that an exhaustive approach to rule learning in DNF results in significantly smaller classifiers than those of the other rule learners, while securing comparable or even better performance. Clearly, exhaustive search is computer-intensive and may not always be feasible. Nevertheless, based on this study, we believe that exhaustive search should be considered an alternative for greedy search in many problems

Decision Tables: Scalable Classification Exploring RDBMS Capabilities

In this paper, we report our success in building efficient scalable classifiers in the form of decision tables by exploring capabilities of modern relational database management systems. In addition to high classification accuracy, the unique features of the approach include its high training speed, linear scalability, and simplicity in implementation. More importantly, the major computation required in the approach can be implemented using standard functions provided by the modern relational DBMS. This not only makes implementation of the classifier extremely easy, further performance improvement is also expected when better processing strategies for those computations are developed and implemented in RDBMS. The novel classification approach based on grouping and counting and its implementation on top of RDBMS is described. The result

Doctor of Philosophy

dissertationWith the growing national dissemination of the electronic health record (EHR), there are expectations that the public will benefit from biomedical research and discovery enabled by electronic health data. Clinical data are needed for many diseases and conditions to meet the demands of rapidly advancing genomic and proteomic research. Many biomedical research advancements require rapid access to clinical data as well as broad population coverage. A fundamental issue in the secondary use of clinical data for scientific research is the identification of study cohorts of individuals with a disease or medical condition of interest. The problem addressed in this work is the need for generalized, efficient methods to identify cohorts in the EHR for use in biomedical research. To approach this problem, an associative classification framework was designed with the goal of accurate and rapid identification of cases for biomedical research: (1) a set of exemplars for a given medical condition are presented to the framework, (2) a predictive rule set comprised of EHR attributes is generated by the framework, and (3) the rule set is applied to the EHR to identify additional patients that may have the specified condition. iv Based on this functionality, the approach was termed the ‘cohort amplification' framework. The development and evaluation of the cohort amplification framework are the subject of this dissertation. An overview of the framework design is presented. Improvements to some standard associative classification methods are described and validated. A qualitative evaluation of predictive rules to identify diabetes cases and a study of the accuracy of identification of asthma cases in the EHR using frameworkgenerated prediction rules are reported. The framework demonstrated accurate and reliable rules to identify diabetes and asthma cases in the EHR and contributed to methods for identification of biomedical research cohorts

Association Pattern Analysis for Pattern Pruning, Clustering and Summarization

Automatic pattern mining from databases and the analysis of the discovered patterns for useful information are important and in great demand in science, engineering and business. Today, effective pattern mining methods, such as association rule mining and pattern discovery, have been developed and widely used in various challenging industrial and business applications. These methods attempt to uncover the valuable information trapped in large collections of raw data. The patterns revealed provide significant and useful information for decision makers. Paradoxically, pattern mining itself can produce such huge amounts of data that poses a new knowledge management problem: to tackle thousands or even more patterns discovered and held in a data set. Unlike raw data, patterns often overlap, entangle and interrelate to each other in the databases. The relationship among them is usually complex and the notion of distance between them is difficult to qualify and quantify. Such phenomena pose great challenges to the existing data mining discipline. In this thesis, the analysis of patterns after their discovery by existing pattern mining methods is referred to as pattern post-analysis since the patterns to be analyzed are first discovered. Due to the overwhelmingly huge volume of discovered patterns in pattern mining, it is virtually impossible for a human user to manually analyze them. Thus, the valuable trapped information in the data is shifted to a large collection of patterns. Hence, to automatically analyze the patterns discovered and present the results in a user-friendly manner such as pattern post-analysis is badly needed. This thesis attempts to solve the problems listed below. It addresses 1) the important factors contributing to the interrelating relationship among patterns and hence more accurate measurements of distances between them; 2) the objective pruning of redundant patterns from the discovered patterns; 3) the objective clustering of the patterns into coherent pattern clusters for better organization; 4) the automatic summarization of each pattern cluster for human interpretation; and 5) the application of pattern post-analysis to large database analysis and data mining. In this thesis, the conceptualization, theoretical formulation, algorithm design and system development of pattern post-analysis of categorical or discrete-valued data is presented. It starts with presenting a natural dual relationship between patterns and data. The relationship furnishes an explicit one-to-one correspondence between a pattern and its associated data and provides a base for an effective analysis of patterns by relating them back to the data. It then discusses the important factors that differentiate patterns and formulates the notion of distances among patterns using a formal graphical approach. To accurately measure the distances between patterns and their associated data, both the samples and the attributes matched by the patterns are considered. To achieve this, the distance measure between patterns has to account for the differences of their associated data clusters at the attribute value (i.e. item) level. Furthermore, to capture the degree of variation of the items matched by patterns, entropy-based distance measures are developed. It attempts to quantify the uncertainty of the matched items. Such distances render an accurate and robust distance measurement between patterns and their associated data. To understand the properties and behaviors of the new distance measures, the mathematical relation between the new distances and the existing sample-matching distances is analytically derived. The new pattern distances based on the dual pattern-data relationship and their related concepts are used and adapted to pattern pruning, pattern clustering and pattern summarization to furnish an integrated, flexible and generic framework for pattern post-analysis which is able to meet the challenges of today’s complex real-world problems. In pattern pruning, the system defines the amount of redundancy of a pattern with respect to another pattern at the item level. Such definition generalizes the classical closed itemset pruning and maximal itemset pruning which define redundancy at the sample level. A new generalized itemset pruning method is developed using the new definition. It includes the closed and maximal itemsets as two extreme special cases and provides a control parameter for the user to adjust the tradeoff between the number of patterns being pruned and the amount of information loss after pruning. The mathematical relation between the proposed generalized itemsets and the existing closed and maximal itemsets are also given. In pattern clustering, a dual clustering method, known as simultaneous pattern and data clustering, is developed using two common yet very different types of clustering algorithms: hierarchical clustering and k-means clustering. Hierarchical clustering generates the entire clustering hierarchy but it is slow and not scalable. K-means clustering produces only a partition so it is fast and scalable. They can be used to handle most real-world situations (i.e. speed and clustering quality). The new clustering method is able to simultaneously cluster patterns as well as their associated data while maintaining an explicit pattern-data relationship. Such relationship enables subsequent analysis of individual pattern clusters through their associated data clusters. One important analysis on a pattern cluster is pattern summarization. In pattern summarization, to summarize each pattern cluster, a subset of the representative patterns will be selected for the cluster. Again, the system measures how representative a pattern is at the item level and takes into account how the patterns overlap each other. The proposed method, called AreaCover, is extended from the well-known RuleCover algorithm. The relationship between the two methods is given. AreaCover is less prone to yield large, trivial patterns (large patterns may cause summary that is too general and not informative enough), and the resulting summary is more concise (with less duplicated attribute values among summary patterns) and more informative (describing more attribute values in the cluster and have longer summary patterns). The thesis also covers the implementation of the major ideas outlined in the pattern post-analysis framework in an integrated software system. It ends with a discussion on the experimental results of pattern post-analysis on both synthetic and real-world benchmark data. Compared with the existing systems, the new methodology that this thesis presents stands out, possessing significant and superior characteristics in pattern post-analysis and decision support

Brute-Force Mining of High-Confidence Classification Rules

This paper investigates a brute-force technique for mining classification rules from large data sets. We employ an association rule miner enhanced with new prun ing strategies to control combinatorial explosion in the number of candidates counted with each database pass. The approach effectively and efficiently extracts high confidence classification rules that apply to most if not all of the data in several classification benchmarks. Introduction Several data mining tasks require dividing up the entities of a database into various classes. Junk-mailers are wellknown users of classification technology, using it to avoid sending out flyers to persons unlikely to be interested in the product being promoted. The task requires a classifier that is usually automatically generated from a "training database " of pre-classified entities. Several approaches have appeared in the AI, statistics, and data-mining literature, and some methods made to scale to large data sets [Shafer et al. 96]. B..

Tree algorithms for mining association rules

With the increasing reliability of digital communication, the falling cost of hardware and increased computational power, the gathering and storage of data has become easier than at any other time in history. Commercial and public agencies are able to hold extensive records about all aspects of their operations. Witness the proliferation of point of sale (POS) transaction recording within retailing, digital storage of census data and computerized hospital records. Whilst the gathering of such data has uses in terms of answering specific queries and allowing visulisation of certain trends the volumes of data can hide significant patterns that would be impossible to locate manually. These patterns, once found, could provide an insight into customer behviour, demographic shifts and patient diagnosis hitherto unseen and unexpected. Remaining competitive in a modem business environment, or delivering services in a timely and cost effective manner for public services is a crucial part of modem economics. Analysis of the data held by an organisaton, by a system that "learns" can allow predictions to be made based on historical evidence. Users may guide the process but essentially the software is exploring the data unaided. The research described within this thesis develops current ideas regarding the exploration of large data volumes. Particular areas of research are the reduction of the search space within the dataset and the generation of rules which are deduced from the patterns within the data. These issues are discussed within an experimental framework which extracts information from binary data