The running time of existing algorithms in Frequent Pattern Mining (FPM) increases exponentially with increasing average data size. The existing algorithms on high dimensional datasets create large number of frequent patterns of small and mid sizes which are ineffective for decision making and shows deficiency on mining process. To discover large patterns or Colossal Patterns Doubleton Pattern Mining (DPM) is considered as very constructive for analyzing these datasets.In this paper, DPM, An integrated approach for discovering Colossal Pattern from Biological datasets is discussed. DPM effectively discovers a set of Colossal Patterns using vertical top-down column intersection operator. DPM makes use of a data structure called 'D-struct', as combination of a doubleton data matrix and one dimensional array pair set to dynamically discover Colossal Patterns from Biological datasets. D-struct has a diverse feature to facilitate is, it has extremely limited and accurately predictable main memory and runs very quickly in memory based constraints. The algorithm is designed in such a way that it enumerates D-struct matrix iteratively and constructs a phylogenetic tree to discover colossal patterns and takes only one scan over the database. The empirical analysis on DPM shows that, the proposed approach attains a better mining efficiency on various Biological datasets and outperforms Colossal Pattern Miner (CPM) in different settings.

At present, due to the developments in Database Technology, large volumes of data are produced by everyday operations and they have introduced the necessity of representing the data in High Dimensional Datasets. Discovering Frequent Determinant Patterns and Association Rules from these High Dimensional Datasets has become very tedious since these databases contain large number of different attributes. For the reason that, it generates extremely large number of redundant rules which makes the algorithms inefficient and it does not fit in main memory.In this paper, a new Association Rule Mining approach is presented, and it efficiently discovers Frequent Determinant Patterns and Association Rules from High Dimensional Datasets. The proposed approach adopts the conventional Apriori algorithm and device anew CApriori algorithm to prune the generated Frequent Determinant Sets effectively. A Frequent Determinant set is selected if its value is first compared with Conviction threshold value and then compared with Support threshold. This double comparison will eliminate the redundancy and generate strong Association Rules.To improve the mining process, this algorithm also makes use of a compressed data structure f_list constructed from feature attributes selected using Heuristic Fitness Function (HFF) and a Heuristic Discretization algorithm. It also makes use of Count Array (CA) devised as One Dimensional Triple Array pair set to minimize main memory utilization. This comprehensive study shows that the approach outperforms with traditional Apriori and obtains more rapid computing speed and at the same time generates Sententious Rules. Further the mining methodology is ascertained to be better in generating strong Association Rules from High Dimensional Databases.

Association rule mining aims at generating association rules between sets of items in a database. Now a day, due to huge accumulation in the database technology, the data are representing in the high dimensional data space. However, it is becoming very tedious to generate association rules from high dimensional data, because it contains different dimensions or attributes in the large data bases. In this paper, a method for generating association rules from large high dimensional data is proposed . It constitutes three steps, 1) pre-processing and generalizing the data base; 2) it generates large frequent kdimension set using user supplied support value which is more feasible than the traditional approach; and 3) generating strong association rules using confidence. It can be seen from experiments that the mining algorithm is elegant and efficient, which can obtain more rapid computing speed and sententious rules at the same time It was ascertained that the proposed method is proved to be better in support of generating association rules.

No abstract

Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning methodologies have been significantly systematic in recent years. The research introduces a novel approach using the fully connected committee machine (FCM) and different activation functions to address conceptual changes in continuous data streams. It explores scenarios of continual learning and investigates the effects of over-learning and weight decay on concept drift. The findings demonstrate the effectiveness of the FCM framework and provide insights into improving machine learning approaches for continuous data stream analysis. We used a layered neural network framework to experiment with different scenarios of continual learning on continuous data streams in the presence of change in the data distribution using a fully connected committee machine (FCM). In this research, we conduct experiments in various scenarios using a layered neural network framework, specifically the fully connected committee machine (FCM), to address conceptual changes in continuous data streams for continual learning under a conceptual change in the data distribution. Sigmoidal and ReLU (Rectified Linear Unit) activation functions are considered for learning regression in layered neural networks. When the layered framework is trained from the input data stream, the regression scheme changes consciously in all scenarios. A fully connected committee machine (FCM) is trained to perform the tasks described in continual learning with M hidden units on dynamically generated inputs. In this method, we run Monte Carlo simulations with the same number of units on both sides, K and M, to define the advancement of intersections between several hidden units and the calculation of generalization error. This is applied to over-learnability as a method of over-forgetting, integrating weight decay, and examining its effects when a concept drift is presented.

No abstract

Concept drift is defined as the distributed data across multiple data streams that change over the time. Concept drift is visible only when the type of collected data changes after some stable period. The emergence of concept drift in data streams leads to increase misclassification and performing degradation of data streams. In order to obtain accurate results, identification of such concept drifts must be visible. This paper focused on a review of the issues related to identifying the changes occurred in the various multivariate high dimensional data streams. The insight of the manuscript is probing the inbuilt difficulties of existing contemporary change-detection methods when they encounter during data dimensions scales.

No abstract