Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube and interactively selecting

Quantitative Redundancy in Partial Implications

We survey the different properties of an intuitive notion of redundancy, as a function of the precise semantics given to the notion of partial implication. The final version of this survey will appear in the Proceedings of the Int. Conf. Formal Concept Analysis, 2015.Comment: Int. Conf. Formal Concept Analysis, 201

Analysis study on R-Eclat algorithm in infrequent itemsets mining

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data

Extraction of High Utility Itemsets using Utility Pattern with Genetic Algorithm from OLTP System

To analyse vast amount of data, Frequent pattern mining play an important role in data mining. In practice, Frequent pattern mining cannot meet the challenges of real world problems due to items differ in various measures. Hence an emerging technique called Utility-based data mining is used in data mining processes.The utility mining not only considers the frequency but also see the utility associated with the itemsets.The main objective of utility mining is to extract the itemsets with high utilities, by considering user preferences such as profit,quantity and cost from OLTP systems. In our proposed approach, we are using UP growth with Genetic Algorithm. The idea is that UP growth algorithm would generate Potentially High Utility Itemsets and Genetic Algorithm would optimize and provide the High Utility Item set from it. On comparing with existing algorithm, the proposed approach is performing better in terms of memory utilization. DOI: 10.17762/ijritcc2321-8169.15039

Twitter data analysis by means of Strong Flipping Generalized Itemsets

Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis ofTwitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Aimed at addressing this issue, generalized itemsets - sets of items at different abstraction levels - can be effectively mined and used todiscover interesting multiple-level correlations among data supplied with taxonomies. Each generalizeditemset is characterized by a correlation type (positive, negative, or null) according to the strength of thecorrelation among its items.This paper presents a novel data mining approach to supporting different and interesting targetedanalysis - topic trend analysis, context-aware service profiling - by analyzing Twitter posts. We aim atdiscovering contrasting situations by means of generalized itemsets. Specifically, we focus on comparingitemsets discovered at different abstraction levels and we select large subsets of specific (descendant)itemsets that show correlation type changes with respect to their common ancestor. To this aim, a novelkind of pattern, namely the Strong Flipping Generalized Itemset (SFGI), is extracted from Twitter mes-sages and contextual information supplied with taxonomy hierarchies. Each SFGI consists of a frequentgeneralized itemset X and the set of its descendants showing a correlation type change with respect to X. Experiments performed on both real and synthetic datasets demonstrate the effectiveness of the pro-posed approach in discovering interesting and hidden knowledge from Twitter dat

Mining Meaning from Text by Harvesting Frequent and Diverse Semantic Itemsets

Abstract. In this paper, we present a novel and completely-unsupervised approach to unravel meanings (or senses) from linguistic constructions found in large corpora by introducing the concept of semantic vector. A semantic vector is a space-transformed vector where features repre-sent fine-grained semantic information units, instead of values of co-occurrences within a collection of texts. More in detail, instead of seeing words as vectors of frequency values, we propose to first explode words into a multitude of tiny semantic information retrieved from existing re-sources like WordNet and ConceptNet, and then clustering them into frequent and diverse patterns. This way, on the one hand, we are able to model linguistic data with a larger but much more dense and informa-tive semantic feature space. On the other hand, being the model based on basic and conceptual information, we are also able to generate new data by querying the above-mentioned semantic resources with the fea-tures contained in the extracted patterns. We experimented the idea on a dataset of 640 millions of triples subject-verb-object to automatically inducing senses for specific input verbs, demonstrating the validity and the potential of the presented approach in modeling and understanding natural language

The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems