Abstract

Active learning is a rapidly growing field of machine learning which aims at reducing the labeling effortof the oracle (human expert) in acquiring informa-tive training samples in domains where the costof labeling is high. Associative classification is a well established prediction method which possesses the advantages of high accuracy and faster learning rates in classification. In this paper, we propose a novel algorithm which unifies associative classification with active learning. The algorithm has two major procedures of Rule generation and rule pruning.The algorithm selects unlabeled instances from the pool of available samples and uses a unique dynamic rule sampling procedure for updating the model.The rules are dynamically sampled class association rules (CAR) which are generated using the mined Minimal infrequent itemsets. The results derived over 10 datasets from the UCI-ML repository for our approach have been compared with those from the ACTIVE-DECORATE algorithm. We also analyze our sampling method against the state of art sampling frameworks and show that our method performs better.

Abstract

In recent years, the number of electronic journal articles is growing faster than ever before; information is generated faster than people can deal with it. In order to handle this problem, many electronic periodical databases have proposed keyword search methods to decrease the effort and time spent by users in searching the journal's archives. However, the users still have to deal with a huge number of search results. In this paper, we present the problem of mining landmark papers. We treat papers that introduce important key phrases for the first time as landmark papers. Our approach combines simple ideas from text mining, information extraction and information retrieval to identify landmark papers. We show that existing related techniques such as first story detection, mining hot topics and theme mining do not effectively handle the landmark paper mining problem. Our approach is simpler and more direct for this task. We experimentally evaluate our approach on a large dataset of papers in the database or data mining areas downloaded using DBLP.

Abstract

Most data mining research has focused on devel-oping algorithms to discover statistically significant patternsin large datasets. However, utilizing the resulting patterns indecision support is more of an art, and the utility of such patterns is often questioned. In this paper we formalize a technique that utilizes data mining concepts to recommend an optimal set of items to customers in a retail store based on the contents of their market baskets. The recommended set of items maximizes the expected profit of the store and is decided based on patterns learnt from past transactions. In addition to concepts of clustering and frequent itemsets, the proposed method also combines the idea of knapsack problems to decide on the items to recommend. We empirically compare our approach with existing methods on both real and synthetic datasets and show that our method yields better profits while being faster and simpler.

Abstract

This paper presents a novel architecture PERIC-ASA, PER turbed frequent Itemset based classification for Com-putational Auditory Scene Analysis(CASA). A novel approach for perception of sound waves has been developed. Our aim is to develop a classifier whichcan correctly identify sound waves from noisy sound mixturesi.e. to solve the classical‘Cocktail Party Problem’. The architecture is based on Gestalt principles of grouping like Pragnanz, Proximity, Common Fateand Similarity. These grouping cues are incorporated into a new Classification approach which is based on a concept namely Perturbed Frequent Itemsets. The primary idea is more the ease with which we can identify different feature values, easier it is to identify the sound wave.

Abstract

Conventional Association Rule Mining (ARM) algorithms usually deal with datasets with binary values, and expect any numerical values to be converted to binary ones using sharp partitions, like Age = 25 to 60. In order to mitigate this constraint, Fuzzy logic is used to convert quantitative values of attributes to binary ones, so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate fuzzy association rules. But, before any fuzzy ARM algorithm can be used, the original dataset (with crisp attributes) needs to be transformed into a form with fuzzy attributes. This paper describes a methodology, called FPrep, to do this pre-processing, which first involves using fuzzy clustering to generate fuzzy partitions, and then uses these partitions to get a fuzzy version (with fuzzy records) of the original dataset. Ultimately, the fuzzy data (fuzzy records) are represented in a standard manner such that they can be used as input to any kind of fuzzy ARM algorithm, irrespective of how it works and processes fuzzy data. We also show that FPrep is much faster than other such comparable transformation techniques, which in turn depend on non-fuzzy techniques, like hard clustering (CLARANS and CURE). Moreover, we illustrate the quality of the fuzzy partitions generated using FPrep, and the number of frequent itemsets generated by a fuzzy ARM algorithm when preceded by FPrep.

Abstract

All associative classifiers developed till now are crisp in nature, and thus use sharp partitioning to transform numerical attributes to binary ones like “Income = [100K and above]”. On the other hand, the novel fuzzy associative classification algorithm called FACISME, which we propose in this paper, uses fuzzy logic to convert numerical attributes to fuzzy attributes, like “Income = High”, thus maintaining the integrity of information conveyed by such numerical attributes. Moreover, FACISME is based on maximum entropy, and uses iterative scaling, both of which lend a very strong theoretical foundation to the algorithm. Entropy is one of the best measures of information, and maximum-entropy-based algorithms do not assume independence of parameters in the classification process. Thus, FACISME provides very goodaccuracy, and can work with all types of datasets (irrespective of size and type of attributes – numerical or binary) and domains.

Abstract

Evolutionary clustering is an emerging research area ad-dressing the problem of clustering dynamic data. An evolu-tionary clustering should take care of two conflicting criteria:preserving the current cluster quality and not deviating too much from the recent history. In this paper we propose an al-gorithm for evolutionary clustering using frequent itemsets.A frequent itemset based approach for evolutionary cluster-ing is natural and it automatically satisfy the two criteria of evolutionary clustering. We provide theoretical as well as experimental proofs to support our claims. We performed experiments on our approach using different datasets and the results show that our approach is comparable to most of the existing algorithms for evolutionary clustering

Abstract

The problem of regression is to estimate the value of a de-pendent numeric variable based on the values of one or more independent variables. Regression algorithms are used for prediction (including fore-casting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Although this problem has been studied exten-sively, most of these approaches are not generic in that they require the user to make an intelligent guess about the form of the regression equa-tion. In this paper we present a new regression algorithm PAGER – Pa-rameterless, Accurate, Generic, Efficient kNN-based Regression. PAGER is also simple and outlier-resilient. These desirable features make PAGER a very attractive alternative to existing approaches. Our experimental study compares PAGER with 12 other algorithms on 4 standard real datasets, and shows that PAGER is more accurate than its competitors.

Abstract

In this paper, we consider the problem of mining the special proper-ties of a given record in a relational dataset. In our formulation, a property is a combination of multiple attribute-value pairs. The support of a property is the number of records that satisfy it. We consider a property as special if its support occurs to us as a shockand the measure of this shock factor is more than a userdefined thresholdη. We provide a way to define this notion of shock based on en-tropy. We also output the shock factor for records in the dataset in a convenient,easily-interpretable manner. An illustrated example is provided on how users can interpret the results. Experiments on real and synthetic data sets reveal interesting properties of data records that cannot be mined using traditional approaches.

Abstract

High dimensionality is a major challenge in document clus-tering. Some of the recent algorithms address this problem by using frequent itemsets for clustering. But, most of these algorithms neglect the semantic relationship between the words. On the other hand there are algorithms that take care of the semantic relations between the words by making use of external knowledge contained in WordNet, Mesh,Wikipedia, etc but do not handle the high dimensionality. In this paper we present an efficient solution that addresses both these problems. We propose a hierarchical clustering algorithm using closed frequent itemsets that use Wikipedia as an external knowledge to enhance the document representation. We evaluate our methods based on F-Score on standard datasets and show our results to be better than existing approaches.